Lowering the cost of anonymization

a PhD thesis

Bibliography

1.

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K. & Zhang, L. Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016).

2.

Abowd, J. M. Staring-Down the Database Reconstruction Theorem. Joint Statistical Meetings, Vancouver, BC (2018).

3.

Abowd, J. M. The US Census Bureau adopts differential privacy. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018), p. 2867.

4.

Abowd, J. M., Andersson, F., Graham, M., Vilhuber, L. & Wu, J. Formal Privacy Guarantees and Analytical Validity of OnTheMap Public-Use Data. 2010. https://ecommons.cornell.edu/handle/1813/47672.

5.

Abowd, J. M., Schneider, M. J. & Vilhuber, L. Differential privacy applications to Bayesian and linear mixed model estimation. Journal of Privacy and Confidentiality (2013).

6.

Acharya, J., Bonawitz, K., Kairouz, P., Ramage, D. & Sun, Z. Context Aware Local Differential Privacy. ICML 2020: 37th International Conference on Machine Learning (2020).

7.

Aghasian, E., Garg, S. & Montgomery, J. User’s Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy. arXiv preprint arXiv:1806.07629 (2018).

8.

Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A., Gabrilovich, E., Gadepalli, K., Gipson, B., Guevara, M., et al. Google COVID-19 community mobility reports: Anonymization process description (version 1.0). arXiv preprint arXiv:2004.04145 (2020).

9.

Alaggan, M., Cunche, M. & Gambs, S. Privacy-preserving Wi-Fi analytics. Proceedings on Privacy Enhancing Technologies vol. 2018(2), p. 4 (2018).

10.

Alaggan, M., Gambs, S. & Kermarrec, A.-M. Heterogeneous Differential Privacy. Journal of Privacy and Confidentiality vol. 7(2), p. 6 (2017).

11.

Allen, J., Ding, B., Kulkarni, J., Nori, H., Ohrimenko, O. & Yekhanin, S. An algorithmic framework for differentially private data analysis on trusted processors. Advances in Neural Information Processing Systems (2019).

12.

Alvim, M., Chatzikokolakis, K., Palamidessi, C. & Pazii, A. Local differential privacy on metric spaces: optimizing the trade-off with utility. 2018 IEEE 31st Computer Security Foundations Symposium (CSF) (2018).

13.

Amin, K., Kulesza, A., Munoz, A. & Vassilvtiskii, S. Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. Proceedings of the 36th International Conference on Machine Learning, PMLR 97 (2019), p. 263.

14.

Anderson, N. “Anonymized” data really isn’t—and here’s why not. Ars Technica. (2009).

15.

Andrés, M. E., Bordenabe, N. E., Chatzikokolakis, K. & Palamidessi, C. Geo-indistinguishability: Differential privacy for location-based systems. Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security (2013).

16.

Andrysco, M., Kohlbrenner, D., Mowery, K., Jhala, R., Lerner, S. & Shacham, H. On subnormal floating point and abnormal timing. 2015 IEEE Symposium on Security and Privacy (2015), p. 623.

17.

Apache Beam. https://beam.apache.org/ (Accessed: 2020-09-18).

18.

Approximate algorithms in Apache Spark: HyperLogLog and Quantiles. https://databricks.com/blog/2016/05/19/approximate-algorithms-in-apache-spark-hyperloglog-and-quantiles.html (Accessed: 2020-08-14).

19.

Arram, M. Case Study: Differential Privacy Innovation at Bluecore. https://georgianpartners.com/differential-privacy-innovation-at-bluecore/ (Accessed: 2020-08-18).

20.

ARX Data Anonymization Tool. https://arx.deidentifier.org/ (Accessed: 2020-10-05).

21.

Ashok, V. G. & Mukkamala, R. A scalable and efficient privacy preserving global itemset support approximation using Bloom filters. IFIP Annual Conference on Data and Applications Security and Privacy (2014).

22.

Asi, H., Duchi, J. & Javidbakht, O. Element Level Differential Privacy: The Right Granularity of Privacy. arXiv preprint arXiv:1912.04042 (2019).

23.

Asif, H., Papakonstantinou, P. A. & Vaidya, J. How to Accurately and Privately Identify Anomalies. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (2019).

24.

Backes, M., Kate, A., Meiser, S. & Ruffing, T. Differential Indistinguishability for Cryptography with (Bounded) Weak Sources. Grande Region Security and Reliability Day (GRSRD) (2014).

25.

Balle, B., Barthe, G., Gaboardi, M. & Geumlek, J. Privacy amplification by mixing and diffusion mechanisms. Advances in Neural Information Processing Systems (2019), p. 13298.

26.

Balle, B., Barthe, G., Gaboardi, M., Hsu, J. & Sato, T. Hypothesis Testing Interpretations and Renyi Differential Privacy. AISTATS (2019), p. 2496.

27.

Balle, B., Bell, J., Gascón, A. & Nissim, K. The Privacy Blanket of the Shuffle Model. Annual International Cryptology Conference (2019), p. 638.

28.

Balle, B. & Wang, Y.-X. Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. International Conference on Machine Learning (2018), p. 394.

29.

Bar-Yossef, Z., Jayram, T., Kumar, R., Sivakumar, D. & Trevisan, L. Counting distinct elements in a data stream. International Workshop on Randomization and Approximation Techniques in Computer Science (2002).

30.

Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F. & Talwar, K. Privacy, accuracy, and consistency too: a holistic solution to contingency table release. Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2007), p. 273.

31.

Barber, R. F. & Duchi, J. C. Privacy and statistical risk: Formalisms and minimax bounds. arXiv preprint arXiv:1412.4451 (2014).

32.

Barthe, G., Espitau, T., Grégoire, B., Hsu, J., Stefanesco, L. & Strub, P.-Y. Relational reasoning via probabilistic coupling. Logic for Programming, Artificial Intelligence, and Reasoning (2015), p. 387.

33.

Barthe, G., Gaboardi, M., Hsu, J. & Pierce, B. Programming language techniques for differential privacy. ACM SIGLOG News vol. 3(1), p. 34 (2016).

34.

BASE – Bielefeld Academic Search Engine. https://www.base-search.net/ (Accessed: 2020-10-07).

35.

Bassily, R. & Freund, Y. Typical stability. arXiv preprint arXiv:1604.03336 (2016).

36.

Bassily, R., Groce, A., Katz, J. & Smith, A. Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy. Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on (2013).

37.

Bassily, R., Nissim, K., Smith, A., Steinke, T., Stemmer, U. & Ullman, J. Algorithmic stability for adaptive data analysis. Proceedings of the forty-eighth annual ACM symposium on Theory of Computing (2016).

38.

Bassily, R. & Smith, A. Local, private, efficient protocols for succinct histograms. Proceedings of the forty-seventh annual ACM symposium on Theory of computing (2015), p. 127.

39.

Bassily, R., Smith, A. & Thakurta, A. Private empirical risk minimization: Efficient algorithms and tight error bounds. 2014 IEEE 55th Annual Symposium on Foundations of Computer Science (2014), p. 464.

40.

Bassily, R., Stemmer, U., Thakurta, A. G., et al. Practical locally private heavy hitters. Advances in Neural Information Processing Systems (2017), p. 2288.

41.

Basu, D., Dimitrakakis, C. & Tossou, A. Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost? arXiv preprint arXiv:1905.12298 (2019).

42.

Bater, J., He, X., Ehrich, W., Machanavajjhala, A. & Rogers, J. Shrinkwrap: efficient SQL query processing in differentially private data federations. Proceedings of the VLDB Endowment (2018).

43.

Bavadekar, S., Dai, A., Davis, J., Desfontaines, D., Eckstein, I., Everett, K., Fabrikant, A., Flores, G., Gabrilovich, E., Gadepalli, K., Glass, S., Huang, R., Kamath, C., Kraft, D., Kumok, A., Marfatia, H., Mayer, Y., Miller, B., Pearce, A., Perera, I. M., Ramachandran, V., Raman, K., Roessler, T., Shafran, I., Shekel, T., Stanton, C., Stimes, J., Sun, M., Wellenius, G. & Zoghi, M. Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0). arXiv preprint arXiv:2009.01265 (2020).

44.

Ben-Or, M. & Hassidim, A. The Bayesian learner is optimal for noisy binary search (and pretty good for quantum as well). 49th Annual IEEE Symposium on Foundations of Computer Science (2008), p. 221.

45.

Beyer, K., Haas, P. J., Reinwald, B., Sismanis, Y. & Gemulla, R. On synopses for distinct-value estimation under multiset operations. Proceedings of the 2007 ACM SIGMOD international conference on Management of data (2007).

46.

Bhaskar, R., Bhowmick, A., Goyal, V., Laxman, S. & Thakurta, A. Noiseless database privacy. International Conference on the Theory and Application of Cryptology and Information Security (2011).

47.

Bichsel, B., Gehr, T., Drachsler-Cohen, D., Tsankov, P. & Vechev, M. DP-finder: Finding differential privacy violations by sampling and optimization. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018), p. 508.

48.

BigQuery Technical Documentation on Functions & Operators. https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators (Accessed: 2020-08-18).

49.

Bindschaedler, V., Shokri, R. & Gunter, C. A. Plausible deniability for privacy-preserving data synthesis. Proceedings of the VLDB Endowment vol. 10(5), p. 481 (2017).

50.

Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J. & Seefeld, B. Prochlo: Strong privacy for analytics in the crowd. Proceedings of the 26th Symposium on Operating Systems Principles (2017).

51.

Bittner, D. M., Sarwate, A. D. & Wright, R. N. Using Noisy Binary Search for Differentially Private Anomaly Detection. International Symposium on Cyber Security Cryptography and Machine Learning (2018).

52.

Blocki, J., Blum, A., Datta, A. & Sheffet, O. Differentially private data analysis of social networks via restricted sensitivity. Proceedings of the 4th conference on Innovations in Theoretical Computer Science (2013).

53.

Blum, A., Ligett, K. & Roth, A. A learning theory approach to noninteractive database privacy. Journal of the ACM (JACM) (2013).

54.

Brickell, J. & Shmatikov, V. The cost of privacy: destruction of data-mining utility in anonymized data publishing. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (2008), p. 70.

55.

Broder, A. On the Resemblance and Containment of Documents. Proceedings of the Compression and Complexity of Sequences 1997 (1997), p. 21.

56.

Bun, M., Dwork, C., Rothblum, G. N. & Steinke, T. Composable and versatile privacy via truncated CDP. Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing (2018).

57.

Bun, M. & Steinke, T. Concentrated differential privacy: Simplifications, extensions, and lower bounds. Theory of Cryptography Conference (2016).

58.

Burchard, P. & Daoud, A. Empirical Differential Privacy. arXiv preprint arXiv:1910.12820 (2019).

59.

Campbell-Dollaghan, K. Sorry, your data can still be identified even if it’s anonymized. Fast Company. (2018).

60.

Canard, S. & Olivier, B. Differential Privacy in distribution and instance-based noise mechanisms. IACR Cryptology ePrint Archive (2015).

61.

Canonne, C., Kamath, G. & Steinke, T. The Discrete Gaussian for Differential Privacy. arXiv preprint arXiv:2004.00010 (2020).

62.

Casella, G. & Berger, R. L. Statistical inference (Duxbury Pacific Grove, CA, 2002).

63.

Chan, T. H., Chung, K.-M., Maggs, B. M. & Shi, E. Foundations of differentially oblivious algorithms. Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (2019).

64.

Charest, A.-S. & Hou, Y. On the meaning and limits of empirical differential privacy. Journal of Privacy and Confidentiality (2016).

65.

Charikar, M., Chen, K. & Farach-Colton, M. Finding frequent items in data streams. International Colloquium on Automata, Languages, and Programming (2002), p. 693.

66.

Charikar, M. S. Similarity estimation techniques from rounding algorithms. Proceedings of the thiry-fourth annual ACM symposium on Theory of computing (2002), p. 380.

67.

Chatzikokolakis, K., Andrés, M. E., Bordenabe, N. E. & Palamidessi, C. Broadening the scope of differential privacy using metrics. International Symposium on Privacy Enhancing Technologies Symposium (2013).

68.

Chatzikokolakis, K., ElSalamouny, E., Palamidessi, C., Anna, P., et al. Methods for Location Privacy: A comparative overview. Foundations and Trends® in Privacy and Security (2017).

69.

Chaudhuri, K., Imola, J. & Machanavajjhala, A. Capacity bounded differential privacy. Advances in Neural Information Processing Systems (2019).

70.

Chaudhuri, K. & Mishra, N. When random sampling preserves privacy. Annual International Cryptology Conference (2006).

71.

Chaudhuri, K., Monteleoni, C. & Sarwate, A. D. Differentially private empirical risk minimization. Journal of Machine Learning Research vol. 12(3) (2011).

72.

Chen, L., Ghazi, B., Kumar, R. & Manurangsi, P. On Distributed Differential Privacy and Counting Distinct Elements. arXiv preprint arXiv:2009.09604 (2020).

73.

Chen, R., Fung, B. C., Yu, P. S. & Desai, B. C. Correlated network data publication via differential privacy. The VLDB Journal—The International Journal on Very Large Data Bases (2014).

74.

Chen, S. & Zhou, S. Recursive mechanism: towards node differential privacy and unrestricted joins. Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (2013).

75.

Chen, Z., Bao, X., Ying, Z., Liu, X. & Zhong, H. Differentially Private Location Protection with Continuous Time Stamps for VANETs. International Conference on Algorithms and Architectures for Parallel Processing (2018).

76.

Chia, P. H., Desfontaines, D., Perera, I. M., Simmons-Marengo, D., Li, C., Day, W.-Y., Wang, Q. & Guevara, M. KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale. 2019 IEEE Symposium on Security and Privacy (SP) (2019), p. 350.

77.

Choi, S. G., Dachman-Soled, D., Kulkarni, M. & Yerukhimovich, A. Differentially-Private Multi-Party Sketching for Large-Scale Statistics. Proceedings on Privacy Enhancing Technologies vol. 3, p. 153 (2020).

78.

Clifton, C. & Tassa, T. On syntactic anonymity and differential privacy. 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW) (2013).

79.

Cohen, A., Nikolov, A., Schutzman, Z. & Ullman, J. Reconstruction Attacks in Practice. DifferentialPrivacy.org. https://differentialprivacy.org/diffix-attack/. 2020.

80.

Cohen, A. & Nissim, K. Linear Program Reconstruction in Practice. Journal of Privacy and Confidentiality vol. 10(1) (2020).

81.

Colisson, L. L3 Internship report: Quantum analog of Differential Privacy in term of Rényi divergence. (2016).

82.

Computing k-map estimates with Cloud DLP. https://cloud.google.com/dlp/docs/compute-risk-analysis%5C#compute-k-map (Accessed: 2020-10-05).

83.

Computing Private Statistics with Privacy on Beam. https://codelabs.developers.google.com/codelabs/privacy-on-beam/ (Accessed: 2020-09-23).

84.

Cormode, G. & Hadjieleftheriou, M. Finding frequent items in data streams. Proceedings of the VLDB Endowment vol. 1(2), p. 1530 (2008).

85.

Cormode, G. & Muthukrishnan, S. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms vol. 55(1), p. 58 (2005).

86.

Cormode, G. & Muthukrishnan, S. Space efficient mining of multigraph streams. Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2005), p. 271.

87.

Council, T. P. P. TPC-H benchmark specification. http://www.tpc.org/tpch/. 2008.

88.

Cuff, P. & Yu, L. Differential privacy as a mutual information constraint. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016).

89.

Cummings, R. & Durfee, D. Individual sensitivity preprocessing for data privacy. Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms (2020).

90.

Dai Nguyen, T., Gupta, S., Rana, S. & Venkatesh, S. A Privacy Preserving Bayesian Optimization with High Efficiency. Pacific-Asia Conference on Knowledge Discovery and Data Mining (2018).

91.

Dalenius, T. Towards a methodology for statistical disclosure control. statistik Tidskrift (1977).

92.

Dandekar, A., Basu, D. & Bressan, S. Differential Privacy at Risk: Bridging Randomness and Privacy Budget. Proceedings on Privacy Enhancing Technologies, p. 1 (2020).

93.

De Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M. & Blondel, V. D. Unique in the crowd: The privacy bounds of human mobility. Scientific reports vol. 3, p. 1376 (2013).

94.

De Montjoye, Y.-A., Radaelli, L., Singh, V. K., et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science vol. 347(6221), p. 536 (2015).

95.

Dean, J. & Ghemawat, S. MapReduce: a flexible data processing tool. Communications of the ACM vol. 53(1), p. 72 (2010).

96.

Decennial Census of Population and Housing. 2010. https://factfinder.census.gov.

97.

Deldar, F. & Abadi, M. PLDP-TD: Personalized-location differentially private data analysis on trajectory databases. Pervasive and Mobile Computing (2018).

98.

Desfontaines, D. Personal blog. https://desfontain.es/privacy (Accessed: 2020-09-22).

99.

Desfontaines, D., Lochbihler, A. & Basin, D. Cardinality estimators do not preserve privacy. Proceedings on Privacy Enhancing Technologies vol. 2019(2), p. 26 (2019).

100.

Desfontaines, D., Mohammadi, E., Krahmer, E. & Basin, D. Differential privacy with partial knowledge. arXiv preprint arXiv:1905.00650 (2019).

101.

Desfontaines, D. & Pejó, B. Differential Privacies: a taxonomy of differential privacy variants and extensions (long version). arXiv preprint arXiv:1906.01337 (2019).

102.

Desfontaines, D. & Pejó, B. SoK: Differential Privacies. Proceedings on Privacy Enhancing Technologies vol. 2020(2) (2020).

103.

Diakonikolas, I., Gouleakis, T., Peebles, J. & Price, E. Collision-based Testers are Optimal for Uniformity and Closeness. Electronic Colloquium on Computational Complexity vol. 23, p. 178 (2016).

104.

Diaz, C., Seys, S., Claessens, J. & Preneel, B. Towards measuring anonymity. International Workshop on Privacy Enhancing Technologies (2002), p. 54.

105.

Diffprivlib: IBM’s differential privacy library. https://github.com/IBM/differential-privacy-library (Accessed: 2020-08-18).

106.

Dimitrakakis, C., Nelson, B., Zhang, Z., Mitrokotsa, A. & Rubinstein, B. I. P. Differential privacy for bayesian inference through posterior sampling. Journal of Machine Learning Research vol. 18(1), p. 343 (2017).

107.

Ding, B., Kulkarni, J. & Yekhanin, S. Collecting telemetry data privately. Advances in Neural Information Processing Systems (2017).

108.

Ding, X., Wang, W., Wan, M. & Gu, M. Seamless Privacy: Privacy-Preserving Subgraph Counting in Interactive Social Network Analysis. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2013 International Conference on (2013).

109.

Ding, Z., Wang, Y., Wang, G., Zhang, D. & Kifer, D. Detecting Violations of Differential Privacy. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (ACM, Toronto, Canada, 2018), p. 475.

110.

Dinur, I. & Nissim, K. Revealing information while preserving privacy. Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2003).

111.

Dixit, K., Jha, M., Raskhodnikova, S. & Thakurta, A. Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy. Theory of Cryptography - Lecture Notes in Computer Science (2013).

112.

Dobbe, R., Pu, Y., Zhu, J., Ramchandran, K. & Tomlin, C. Customized Local Differential Privacy for Multi-Agent Distributed Optimization. arXiv preprint arXiv:1806.06035 (2018).

113.

Dong, J., Roth, A. & Su, J. W. Gaussian differential privacy. arXiv preprint arXiv:1905.02383 (2019).

114.

Dong, J., Durfee, D. & Rogers, R. Optimal Differential Privacy Composition for Exponential Mechanisms. ICML 2020: 37th International Conference on Machine Learning (2020).

115.

Dong, K., Guo, T., Ye, H., Li, X. & Ling, Z. On the limitations of existing notions of location privacy. Future Generation Computer Systems (2018).

116.

Duan, Y. Privacy without noise. Proceedings of the 18th ACM conference on Information and knowledge management (2009).

117.

Duchi, J. C., Jordan, M. I. & Wainwright, M. J. Local privacy and statistical minimax rates. Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on (2013).

118.

Duchi, J. C. & Ruan, F. The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information. arXiv preprint arXiv:1806.05756 (2018).

119.

Du Pin Calmon, F. & Fawaz, N. Privacy against statistical inference. Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on (2012).

120.

Durand, M. & Flajolet, P. Loglog counting of large cardinalities. European Symposium on Algorithms (2003).

121.

Durfee, D. & Rogers, R. M. Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. Advances in Neural Information Processing Systems (2019).

122.

Dwork, C. Differential Privacy. Proceedings of the 33rd international conference on Automata, Languages and Programming (2006).

123.

Dwork, C. Differential privacy: A survey of results. International Conference on Theory and Applications of Models of Computation (2008).

124.

Dwork, C. The differential privacy frontier. Theory of Cryptography Conference (2009).

125.

Dwork, C. Differential privacy in new settings. Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms (2010).

126.

Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I. & Naor, M. Our Data, Ourselves: Privacy Via Distributed Noise Generation. Eurocrypt (2006).

127.

Dwork, C. & Lei, J. Differential privacy and robust statistics. Proceedings of the forty-first annual ACM symposium on Theory of computing (2009), p. 371.

128.

Dwork, C., McSherry, F., Nissim, K. & Smith, A. Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference (2006).

129.

Dwork, C., Naor, M., Pitassi, T. & Rothblum, G. N. Differential privacy under continual observation. Proceedings of the forty-second ACM symposium on Theory of computing (2010).

130.

Dwork, C., Naor, M., Pitassi, T., Rothblum, G. N. & Yekhanin, S. Pan-Private Streaming Algorithms. ICS (2010), p. 66.

131.

Dwork, C., Roth, A., et al. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science (2014).

132.

Dwork, C. & Rothblum, G. N. Concentrated differential privacy. arXiv preprint arXiv:1603.01887 (2016).

133.

Dwork, C., Rothblum, G. N. & Vadhan, S. Boosting and differential privacy. Foundations of Computer Science (FOCS), 2010 51st Annual IEEE Symposium on (2010), p. 51.

134.

Ebadi, H., Sands, D. & Schneider, G. Differential privacy: Now it’s getting personal. Acm Sigplan Notices (2015).

135.

Eckersley, P. How unique is your web browser? International Symposium on Privacy Enhancing Technologies Symposium (2010), p. 1.

136.

Egert, R., Fischlin, M., Gens, D., Jacob, S., Senker, M. & Tillmanns, J. Privately computing set-union and set-intersection cardinality via Bloom filters. Australasian Conference on Information Security and Privacy (2015).

137.

El Emam, K. & Dankar, F. K. Protecting privacy using k-anonymity. Journal of the American Medical Informatics Association (2008).

138.

ElSalamouny, E. & Gambs, S. Differential privacy models for location-based services. Transactions on Data Privacy (2016).

139.

Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Song, S., Talwar, K. & Thakurta, A. Encode, shuffle, analyze privacy revisited: formalizations and empirical evaluation. arXiv preprint arXiv:2001.03618 (2020).

140.

Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K. & Thakurta, A. Amplification by shuffling: From local to central differential privacy via anonymity. Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (2019), p. 2468.

141.

Erlingsson, Ú., Pihur, V. & Korolova, A. RAPPOR: Randomized aggregatable privacy-preserving ordinal response. Proceedings of the 2014 ACM SIGSAC conference on computer and communications security (2014).

142.

Estan, C., Varghese, G. & Fisk, M. Bitmap algorithms for counting active flows on high speed links. Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement (2003).

143.

Evaluating KHLL accuracy with BigQuery. https://github.com/google/khll-paper-experiments. (Accessed: 2020-09-08).

144.

Evfimievski, A., Gehrke, J. & Srikant, R. Limiting privacy breaches in privacy preserving data mining. Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2003).

145.

Fang, C. & Chang, E.-C. Differential privacy with delta-neighbourhood for spatial and dynamic datasets. Proceedings of the 9th ACM symposium on Information, computer and communications security (2014).

146.

Fanti, G., Pihur, V. & Erlingsson, Ú. Building a rappor with the unknown: Privacy-preserving learning of associations and data dictionaries. Proceedings on Privacy Enhancing Technologies vol. 2016(3), p. 41 (2016).

147.

Farokhi, F. Noiseless Privacy. arXiv preprint arXiv:1910.13027 (2019).

148.

Farokhi, F. Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS) (2020), p. 1.

149.

Feldman, V., Mironov, I., Talwar, K. & Thakurta, A. Privacy amplification by iteration. 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) (2018).

150.

Feldman, V. & Steinke, T. Calibrating noise to variance in adaptive data analysis. Proceedings of Machine Learning Research (2018).

151.

Fernandes, N., Dras, M. & McIver, A. Generalised differential privacy for text document processing. International Conference on Principles of Security and Trust (2019).

152.

Flajolet, P., Fusy, É., Gandouet, O. & Meunier, F. HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm. DMTCS Proceedings (2008).

153.

Flajolet, P. & Martin, G. N. Probabilistic counting. 24th Annual Symposium on Foundations of Computer Science (sfcs 1983) (1983), p. 76.

154.

Flajolet, P. & Martin, G. N. Probabilistic counting algorithms for data base applications. Journal of computer and system sciences (1985).

155.

Francis, P., Eide, S. P. & Munz, R. Diffix: High-utility database anonymization. Annual Privacy Forum (2017), p. 141.

156.

Gaboardi, M., Hay, M. & Vadhan, S. A Programming Framework for OpenDP (2020).

157.

Gadotti, A., Houssiau, F., Rocher, L., Livshits, B. & De Montjoye, Y.-A. When the signal is in the noise: exploiting diffix’s sticky noise. 28th USENIX Security Symposium (USENIX Security 19) (2019), p. 1081.

158.

Ganta, S. R., Kasiviswanathan, S. P. & Smith, A. Composition attacks and auxiliary information in data privacy. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (2008).

159.

Garfinkel, S. L., Abowd, J. M. & Powazek, S. Issues Encountered Deploying Differential Privacy. Proceedings of the 2018 Workshop on Privacy in the Electronic Society (2018).

160.

Gehrke, J., Hay, M., Lui, E. & Pass, R. Advances in Cryptology–CRYPTO 2012 (Springer, 2012).

161.

Gehrke, J., Lui, E. & Pass, R. Towards privacy for social networks: A zero-knowledge based definition of privacy. Theory of Cryptography Conference (2011).

162.

Geumlek, J. & Chaudhuri, K. Profile-based Privacy for Locally Private Computations. 2019 IEEE International Symposium on Information Theory (ISIT) (2019), p. 537.

163.

Geumlek, J., Song, S. & Chaudhuri, K. Renyi differential privacy mechanisms for posterior sampling. Advances in Neural Information Processing Systems (2017).

164.

Ghosh, A. & Kleinberg, R. Inferential Privacy Guarantees for Differentially Private Mechanisms. 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). vol. 67 (2017), p. 3.

165.

Ghosh, A. & Roth, A. Selling privacy at auction. Games and Economic Behavior (2015).

166.

Ghosh, A., Roughgarden, T. & Sundararajan, M. Universally utility-maximizing privacy mechanisms. SIAM Journal on Computing vol. 41(6), p. 1673 (2012).

167.

Gilbert, A. C. & Mcmillan, A. Property Testing For Differential Privacy. 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (2018), p. 249.

168.

Gkoulalas-Divanis, A., Loukides, G. & Sun, J. Publishing data from electronic health records while preserving privacy: A survey of algorithms. Journal of biomedical informatics vol. 50, p. 4 (2014).

169.

Goldwasser, S. & Micali, S. Probabilistic encryption. Journal of computer and system sciences (1984).

170.

Goldwasser, S., Micali, S. & Rackoff, C. The knowledge complexity of interactive proof systems. SIAM Journal on computing (1989).

171.

Golle, P. & Partridge, K. On the anonymity of home/work location pairs. Pervasive computing (2009).

172.

Google Scholar. https://scholar.google.com/ (Accessed: 2020-10-07).

173.

Google’s Differential Privacy library. https://github.com/google/differential-privacy (Accessed: 2020-08-18).

174.

Gotz, M., Machanavajjhala, A., Wang, G., Xiao, X. & Gehrke, J. Publishing search logs—a comparative study of privacy guarantees. IEEE Transactions on Knowledge and Data Engineering vol. 24(3), p. 520 (2011).

175.

Grant, H. “If I die, that is OK”: the Calais refugees with nowhere to turn. The Guardian. (2020).

176.

Grining, K. & Klonowski, M. Towards Extending Noiseless Privacy: Dependent Data and More Practical Approach. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security (2017), p. 546.

177.

Groce, A., Katz, J. & Yerukhimovich, A. Limits of computational differential privacy in the client/server setting. Theory of Cryptography Conference (2011).

178.

Guerraoui, R., Kermarrec, A.-M., Patra, R. & Taziki, M. D 2 p: distance-based differential privacy in recommenders. Proceedings of the VLDB Endowment (2015).

179.

Gulhane, P., Gopi, S., Kulkarni, J., Shen, J. H., Shokouhi, M. & Yekhanin, S. Differentially Private Set Union. ICML 2020: 37th International Conference on Machine Learning (2020).

180.

Gursoy, M. E., Tamersoy, A., Truex, S., Wei, W. & Liu, L. Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy. IEEE Transactions on Dependable and Secure Computing, p. 1 (2019).

181.

Haeberlen, A., Pierce, B. C. & Narayan, A. Differential Privacy Under Fire. USENIX Security Symposium. vol. 33 (2011).

182.

Haitner, I., Mazor, N., Shaltiel, R. & Silbak, J. Channels of Small Log-Ratio Leakage and Characterization of Two-Party Differentially Private Computation. Theory of Cryptography Conference (2019).

183.

Halevy, A., Korn, F., Noy, N. F., Olston, C., Polyzotis, N., Roy, S. & Whang, S. E. Goods: Organizing google’s datasets. Proceedings of the 2016 International Conference on Management of Data (2016), p. 795.

184.

Hall, R. New Statistical Applications for Differential Privacy. PhD thesis (PhD thesis, Carnegie Mellon, 2012).

185.

Hall, R. J., Wasserman, L. A. & Rinaldo, A. Random Differential Privacy. Journal of Privacy and Confidentiality vol. 4(2), p. 3 (2013).

186.

Halton, J. H. Algorithm 247: Radical-inverse Quasi-random Point Sequence. Commun. ACM vol. 7(12), p. 701. (1964).

187.

Haney, S., Machanavajjhala, A. & Ding, B. Design of policy-aware differentially private algorithms. Proceedings of the VLDB Endowment (2015).

188.

Hay, M., Li, C., Miklau, G. & Jensen, D. Accurate estimation of the degree distribution of private networks. Data Mining, 2009. ICDM’09. Ninth IEEE International Conference on (2009).

189.

Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y. & Zhang, D. Principled evaluation of differentially private algorithms using dpbench. Proceedings of the 2016 International Conference on Management of Data (2016), p. 139.

190.

Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y., Zhang, D. & Bissias, G. Exploring privacy-accuracy tradeoffs using dpcomp. Proceedings of the 2016 International Conference on Management of Data (2016), p. 2101.

191.

Hayes, J., Melis, L., Danezis, G. & De Cristofaro, E. LOGAN: Membership inference attacks against generative models. Proceedings on Privacy Enhancing Technologies vol. 2019(1), p. 133 (2019).

192.

Hazy. https://hazy.com/ (Accessed: 2020-08-18).

193.

He, X., Machanavajjhala, A. & Ding, B. Blowfish privacy: Tuning privacy-utility trade-offs using policies. Proceedings of the 2014 ACM SIGMOD international conference on Management of data (2014).

194.

He, X., Machanavajjhala, A., Flynn, C. & Srivastava, D. Composing Differential Privacy and Secure Computation: A case study on scaling private record linkage. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (2017).

195.

Heule, S., Nunkesser, M. & Hall, A. HyperLogLog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm. Proceedings of the 16th International Conference on Extending Database Technology (2013).

196.

Heurix, J., Zimmermann, P., Neubauer, T. & Fenz, S. A taxonomy for privacy enhancing technologies. Computers & Security (2015).

197.

Holohan, N., Antonatos, S., Braghin, S. & Mac Aonghusa, P. (k,e)-Anonymity: k-Anonymity with e-Differential Privacy. arXiv preprint arXiv:1710.01615 (2017).

198.

How Google Anonymizes Data. https://policies.google.com/technologies/anonymization (Accessed: 2020-08-18).

199.

How the Census Bureau Protects Your Data. https://2020census.gov/en/data-protection.html (Accessed: 2020-08-18).

200.

Hsu, J., Gaboardi, M., Haeberlen, A., Khanna, S., Narayan, A., Pierce, B. C. & Roth, A. Differential privacy: An economic method for choosing epsilon. 2014 IEEE 27th Computer Security Foundations Symposium (2014).

201.

Huang, Y. & Dai, H. Quantifying Differential Privacy of Gossip Protocols in General Networks. arXiv preprint arXiv:1905.07598 (2019).

202.

Huber, M., Müller-Quade, J. & Nilges, T. Number Theory and Cryptography (Springer, 2013).

203.

HyperLogLog implementation for mssql. https://github.com/shuvava/mssql-hll (Accessed: 2020-08-18).

204.

International Classification of Diseases, 10th Revision. https://www.who.int/classifications/icd/icdonlineversions/en/ (Accessed: 2020-08-18).

205.

Jaccard, P. Lois de distribution florale dans la zone alpine. vol. 38, p. 69 (1902).

206.

Jelasity, M. & Birman, K. P. Distributional differential privacy for large-scale smart metering. Proceedings of the 2nd ACM workshop on Information hiding and multimedia security (2014).

207.

Jiang, B., Li, M. & Tandon, R. Context-aware Data Aggregation with Localized Information Privacy. 2018 IEEE Conference on Communications and Network Security (CNS) (2018), p. 1.

208.

Johnson, N. & Near, J. P. Dataflow analysis & differential privacy for SQL queries. https://github.com/uber/sql-differential-privacy (Accessed: 2019-09-04).

209.

Johnson, N., Near, J. P. & Song, D. Towards practical differential privacy for SQL queries. Proceedings of the VLDB Endowment (2018).

210.

Johnson, N., Near, J. P., Hellerstein, J. M. & Song, D. Chorus: Differential Privacy via Query Rewriting. arXiv preprint arXiv:1809.07750 (2018).

211.

Jones, A., Leahy, K. & Hale, M. Towards differential privacy for symbolic systems. 2019 American Control Conference (ACC) (2019).

212.

Jorgensen, Z., Yu, T. & Cormode, G. Conservative or liberal? personalized differential privacy. Data Engineering (ICDE), 2015 IEEE 31st International Conference on (2015).

213.

Kairouz, P., Oh, S. & Viswanath, P. The composition theorem for differential privacy. IEEE Transactions on Information Theory (2017).

214.

Karnin, Z., Lang, K. & Liberty, E. Optimal quantile approximation in streams. 2016 ieee 57th annual symposium on foundations of computer science (focs) (2016), p. 71.

215.

Karp, R. M. & Kleinberg, R. Noisy binary search and its applications. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (2007), p. 881.

216.

Kartal, H. B., Liu, X. & Li, X.-B. Differential privacy for the vast majority. ACM Transactions on Management Information Systems (TMIS) (2019).

217.

Kasiviswanathan, S. P. & Smith, A. On the ’semantics’ of differential privacy: A Bayesian formulation. Journal of Privacy and Confidentiality (2014).

218.

Kawamoto, Y. & Murakami, T. Local distribution obfuscation via probability coupling. 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (2019).

219.

Kawamoto, Y. & Murakami, T. Local obfuscation mechanisms for hiding probability distributions. European Symposium on Research in Computer Security (2019).

220.

Kearns, M., Pai, M., Roth, A. & Ullman, J. Mechanism design in large games: Incentives and privacy. Proceedings of the 5th conference on Innovations in theoretical computer science (2014).

221.

Kearns, M., Roth, A., Wu, Z. S. & Yaroslavtsev, G. Private algorithms for the protected in social network search. Proceedings of the National Academy of Sciences (2016).

222.

Kellaris, G., Kollios, G., Nissim, K. & O’Neill, A. Accessing data while preserving privacy. arXiv preprint arXiv:1706.01552 (2017).

223.

Kellaris, G., Papadopoulos, S., Xiao, X. & Papadias, D. Differentially private event sequences over infinite streams. Proceedings of the VLDB Endowment (2014).

224.

Kenthapadi, K. & Tran, T. T. PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (2018).

225.

Kifer, D. & Lin, B.-R. Towards an axiomatization of statistical privacy and utility. Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2010).

226.

Kifer, D. & Lin, B.-R. An axiomatic view of statistical privacy and utility. Journal of Privacy and Confidentiality (2012).

227.

Kifer, D. & Machanavajjhala, A. No free lunch in data privacy. Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (2011), p. 193.

228.

Kifer, D. & Machanavajjhala, A. A rigorous and customizable framework for privacy. Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems (2012).

229.

Kingsley, P. & Shoumali, K. Taking Hard Line, Greece Turns Back Migrants by Abandoning Them at Sea. The New York Times. (2020).

230.

Korolova, A., Kenthapadi, K., Mishra, N. & Ntoulas, A. Releasing search queries and clicks privately. Proceedings of the 18th international conference on World wide web (2009), p. 171.

231.

Kotsogiannis, I., Doudalis, S., Haney, S., Machanavajjhala, A. & Mehrotra, S. One-sided Differential Privacy. 2020 IEEE 36th International Conference on Data Engineering (ICDE) (2020), p. 493.

232.

Kotsogiannis, I., Tao, Y., He, X., Fanaeepour, M., Machanavajjhala, A., Hay, M. & Miklau, G. PrivateSQL: a differentially private SQL query engine. Proceedings of the VLDB Endowment vol. 12(11), p. 1371 (2019).

233.

Kotsogiannis, I., Tao, Y., Machanavajjhala, A., Miklau, G. & Hay, M. Architecting a Differentially Private SQL Engine. Conference on Innovative Data Systems Research (2019).

234.

Krehbiel, S. Choosing Epsilon for Privacy as a Service. Proceedings on Privacy Enhancing Technologies (2019).

235.

Krishnan, V. & Martinez, S. A Probabilistic Framework for Moving-Horizon Estimation: Stability and Privacy Guarantees. IEEE Transactions on Automatic Control, p. 1 (2020).

236.

Latency Analysis Dashboard — Apigee Edge Documentation. https://docs.apigee.com/api-platform/analytics/latency-analysis-dashboard (Accessed: 2020-10-16).

237.

Laud, P. & Pankova, A. Interpreting Epsilon of Differential Privacy in Terms of Advantage in Guessing or Approximating Sensitive Attributes. arXiv preprint arXiv:1911.12777 (2019).

238.

Laud, P., Pankova, A. & Martin, P. Achieving Differential Privacy using Methods from Calculus. arXiv preprint arXiv:1811.06343 (2018).

239.

Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J., Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R., Shan, Y., Canadell, J. G., et al. Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nature Climate Change, p. 1 (2020).

240.

Leahy, S. Most countries aren’t hitting 2030 climate goals, and everyone will pay the price. National Geographic. (2019).

241.

LeapYear. https://leapyear.io/ (Accessed: 2020-08-18).

242.

Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D. & Jana, S. Certified robustness to adversarial examples with differential privacy. 2019 IEEE Symposium on Security and Privacy (S&P) (2019).

243.

Lee, J. & Clifton, C. How much is enough? choosing for differential privacy. International Conference on Information Security (2011).

244.

Lee, J. & Clifton, C. Differential identifiability. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (2012).

245.

Lee, J. & Kifer, D. Concentrated differentially private gradient descent with adaptive per-iteration privacy budget. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018).

246.

LeFevre, K., DeWitt, D. J. & Ramakrishnan, R. Incognito: Efficient full-domain k-anonymity. Proceedings of the 2005 ACM SIGMOD international conference on Management of data (2005).

247.

LeFevre, K., DeWitt, D. J. & Ramakrishnan, R. Mondrian multidimensional k-anonymity. Data Engineering, 2006. ICDE’06. Proceedings of the 22nd International Conference on (2006).

248.

Leung, S. & Lui, E. Bayesian mechanism design with efficiency, privacy, and approximate truthfulness. International Workshop on Internet and Network Economics (2012).

249.

Li, C., Hay, M., Miklau, G. & Wang, Y. A data-and workload-aware algorithm for range queries under differential privacy. Proceedings of the VLDB Endowment vol. 7(5), p. 341 (2014).

250.

Li, J., Khodak, M., Caldas, S. & Talwalkar, A. Differentially Private Meta-Learning. ICLR 2020 : Eighth International Conference on Learning Representations (2020).

251.

Li, N., Li, T. & Venkatasubramanian, S. t-closeness: Privacy beyond k-anonymity and l-diversity. Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on (2007).

252.

Li, N., Lyu, M., Su, D. & Yang, W. Differential privacy: From theory to practice. Synthesis Lectures on Information Security, Privacy, & Trust vol. 8(4), p. 1 (2016).

253.

Li, N., Qardaji, W. & Su, D. On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy. Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security (2012).

254.

Li, N., Qardaji, W., Su, D., Wu, Y. & Yang, W. Membership privacy: a unifying framework for privacy definitions. Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security (2013).

255.

Li, N., Qardaji, W. H. & Su, D. Provably private data anonymization: Or, k-anonymity meets differential privacy. CoRR, abs/1101.2604 (2011).

256.

Li, Y., Ren, X., Yang, S. & Yang, X. Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis. IEEE Transactions on Information Forensics and Security (2019).

257.

Ligett, K., Peale, C. & Reingold, O. Bounded-Leakage Differential Privacy. 1st Symposium on Foundations of Responsible Computing (FORC 2020) (2020).

258.

Liu, C., Chakraborty, S. & Mittal, P. Dependence Makes You Vulnerable: Differential Privacy Under Dependent Tuples. NDSS (2016).

259.

Liu, C., He, X., Chanyaswad, T., Wang, S. & Mittal, P. Investigating statistical privacy frameworks from the perspective of hypothesis testing. Proceedings on Privacy Enhancing Technologies (2019).

260.

Liu, J., Xiong, L. & Luo, J. Semantic Security: Privacy Definitions Revisited. Trans. Data Privacy (2013).

261.

Liu, Z., Wang, Y.-X. & Smola, A. Fast differentially private matrix factorization. Proceedings of the 9th ACM Conference on Recommender Systems (2015).

262.

Long, Y., Bindschaedler, V. & Gunter, C. A. Towards measuring membership privacy. arXiv preprint arXiv:1712.09136 (2017).

263.

Lui, E. & Pass, R. Outlier privacy. Theory of Cryptography Conference (2015).

264.

Machanavajjhala, A., Gehrke, J. & Götz, M. Data publishing against realistic adversaries. Proceedings of the VLDB Endowment (2009).

265.

Machanavajjhala, A., Gehrke, J., Kifer, D. & Venkitasubramaniam, M. l-diversity: Privacy beyond k-anonymity. Data Engineering, 2006. ICDE’06. Proceedings of the 22nd International Conference on (2006).

266.

Machanavajjhala, A. & He, X. Handbook of Mobile Data Privacy (Springer, 2018).

267.

Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J. & Vilhuber, L. Privacy: Theory meets practice on the map. Proceedings of the 2008 IEEE 24th International Conference on Data Engineering (2008).

268.

Manku, G. S., Rajagopalan, S. & Lindsay, B. G. Approximate medians and other quantiles in one pass and with limited memory. ACM SIGMOD Record vol. 27(2), p. 426 (1998).

269.

Marsaglia, G. & Bray, T. A. A convenient method for generating normal variables. SIAM review vol. 6(3), p. 260 (1964).

270.

McClure, D. R. Relaxations of differential privacy and risk/utility evaluations of synthetic data and fidelity measures. PhD thesis (Duke University, 2015).

271.

McMahan, H. B., Ramage, D., Talwar, K. & Zhang, L. Learning Differentially Private Recurrent Language Models. ICLR 2018 : International Conference on Learning Representations 2018 (2018).

272.

McSherry, F. On ”Differential Privacy as a Mutual Information Constraint”. Blog. 2017. https://github.com/frankmcsherry/blog/blob/master/posts/2017-01-26.md.

273.

McSherry, F. D. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (2009), p. 19.

274.

McSherry, F. D. How many secrets do you have? 2017. https://github.com/frankmcsherry/blog/blob/master/posts/2017-02-08.md (Accessed: 2020-10-16).

275.

McSherry, F. D. Synthethic Data via Differential Privacy. https://github.com/frankmcsherry/blog/blob/master/assets/Synth-SIGMOD.pdf (Accessed: 2019-05-28).

276.

McSherry, F. D. Uber’s differential privacy .. probably isn’t. https://github.com/frankmcsherry/blog/blob/master/posts/2018-02-25.md (Accessed: 2019-03-22).

277.

Meiser, S. Approximate and Probabilistic Differential Privacy Definitions. Cryptology ePrint Archive, Report 2018/277 (2018).

278.

Meiser, S. & Mohammadi, E. Tight on Budget? Tight Bounds for r-Fold Approximate Differential Privacy. Proceedings of the 25th ACM Conference on Computer and Communications Security (CCS) (ACM, 2018).

279.

Melis, L., Danezis, G. & Cristofaro, E. D. Efficient Private Statistics with Succinct Sketches. In: Proceedings of the NDSS Symposium 2016. Internet Society: San Diego, CA, USA. (2016) (2016).

280.

Messing, S., DeGregorio, C., Hillenbrand, B., King, G., Mahanti, S., Mukerjee, Z., Nayak, C., Persily, N., State, B. & Wilkins, A. Facebook Privacy-Protected Full URLs Data Set (Harvard Dataverse, 2020). https://doi.org/10.7910/DVN/TDOAPG/DGSAMS.

281.

Metwally, A., Agrawal, D. & El Abbadi, A. Efficient computation of frequent and top-k elements in data streams. International conference on database theory (2005), p. 398.

282.

Meyerson, A. & Williams, R. On the complexity of optimal k-anonymity. Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2004), p. 223.

283.

Mironov, I. On significance of the least significant bits for differential privacy. Proceedings of the 2012 ACM conference on Computer and communications security (2012), p. 650.

284.

Mironov, I. Renyi differential privacy. Computer Security Foundations Symposium (CSF), 2017 IEEE 30th (2017).

285.

Mironov, I., Pandey, O., Reingold, O. & Vadhan, S. Advances in Cryptology-CRYPTO 2009 (Springer, 2009).

286.

Monreale, A., Wang, W. H., Pratesi, F., Rinzivillo, S., Pedreschi, D., Andrienko, G. & Andrienko, N. Geographic Information Science at the Heart of Europe (Springer, 2013).

287.

Morse, J. Sorry, your ’anonymized’ data probably isn’t anonymous. Mashable. (2018).

288.

Murakami, T., Hamada, K., Kawamoto, Y. & Hatano, T. Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces. arXiv preprint arXiv:1911.04226 (2019).

289.

Murakami, T. & Kawamoto, Y. Utility-optimized local differential privacy mechanisms for distribution estimation. 28th USENIX Security Symposium (USENIX Security 19) (2019).

290.

Naldi, M. & D’Acquisto, G. Differential privacy: an estimation theory-based method for choosing epsilon. arXiv preprint arXiv:1510.00917 (2015).

291.

Naor, M. & Vexler, N. Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users. 1st Symposium on Foundations of Responsible Computing (FORC 2020), p. 20 (2020).

292.

Narayan, A., Feldman, A., Papadimitriou, A. & Haeberlen, A. Verifiable differential privacy. Proceedings of the Tenth European Conference on Computer Systems (2015), p. 1.

293.

Narayan, A. & Haeberlen, A. DJoin: differentially private join queries over distributed databases. Presented as part of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12) (2012), p. 149.

294.

Narayanan, A. & Shmatikov, V. Robust de-anonymization of large sparse datasets. 2008 IEEE Symposium on Security and Privacy (S&P) (2008), p. 111.

295.

Nasr, M., Shokri, R. & Houmansadr, A. Machine learning with membership privacy using adversarial regularization. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018), p. 634.

296.

Nelson, B. & Reuben, J. Chasing Accuracy and Privacy, and Catching Both: A Literature Survey on Differentially Private Histogram Publication. arXiv preprint arXiv:1910.14028 (2019).

297.

Nergiz, M. E., Atzori, M. & Clifton, C. Hiding the presence of individuals from shared databases. Proceedings of the 2007 ACM SIGMOD international conference on Management of data (2007).

298.

Nergiz, M. E. & Clifton, C. -presence without complete world knowledge. IEEE Transactions on Knowledge and Data Engineering vol. 22(6), p. 868 (2009).

299.

nextDouble() in Random.java — OpenJDK 8 Source Code. http://hg.openjdk.java.net/jdk8/jdk8/jdk/file/tip/src/share/classes/java/util/Random.java%5C#l531 (Accessed: 2020-10-16).

300.

nextGaussian() in Random.java — OpenJDK 8 Source Code. http://hg.openjdk.java.net/jdk8/jdk8/jdk/file/tip/src/share/classes/java/util/Random.java%5C#l554 (Accessed: 2020-10-16).

301.

Nie, Y., Yang, W., Huang, L., Xie, X., Zhao, Z. & Wang, S. A Utility-optimized Framework for Personalized Private Histogram Estimation. IEEE Transactions on Knowledge and Data Engineering (2018).

302.

Niknami, N., Abadi, M. & Deldar, F. SpatialPDP: A personalized differentially private mechanism for range counting queries over spatial databases. Computer and Knowledge Engineering (ICCKE), 2014 4th International eConference on (2014).

303.

Nissim, K., Raskhodnikova, S. & Smith, A. Smooth sensitivity and sampling in private data analysis. Proceedings of the thirty-ninth annual ACM symposium on Theory of computing (2007).

304.

Nissim, K., Steinke, T., Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., O’Brien, D. R. & Vadhan, S. Differential privacy: A primer for a non-technical audience. Privacy Law Scholars Conf (2017).

305.

normal.go — Go Source Code. https://golang.org/src/math/rand/normal.go (Accessed: 2020-10-16).

306.

normalvariate() in random.py — Python 3.4 Source Code. https://github.com/python/cpython/blob/05c28b08f6e2fc8782472b026c98a3fdd61a2ba9/Lib/random.py%5C#L370 (Accessed: 2020-10-16).

307.

Nozari, E. Networked Dynamical Systems: Privacy, Control, and Cognition. PhD thesis (UC San Diego, 2019).

308.

Open Differential Privacy. https://opendifferentialprivacy.github.io/ (Accessed: 2020-08-18).

309.

Padmanabhan, S., Bhattacharjee, B., Malkemus, T., Cranston, L. & Huras, M. Multi-dimensional clustering: A new data layout scheme in db2. Proceedings of the 2003 ACM SIGMOD international conference on Management of data (2003).

310.

Papapetrou, O., Siberski, W. & Nejdl, W. Cardinality estimation and dynamic length adaptation for Bloom filters. Distributed and Parallel Databases (2010).

311.

Patel, S., Persiano, G. & Yeo, K. What Storage Access Privacy is Achievable with Small Overhead? Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (2019).

312.

Pejo, B., Tang, Q. & Biczók, G. Together or Alone: The Price of Privacy in Collaborative Learning. Proceedings on Privacy Enhancing Technologies (2019).

313.

Percival, K. Federal Laws That Protect Census Confidentiality. https://www.brennancenter.org/sites/default/files/2019-08/Report_Federal_Laws_Census_Confidentiality.pdf (Accessed: 2020-08-18).

314.

Perera, M. & Guevara, M. Improving Usability of Differential Privacy at Scale. https://youtube.com/watch?v=qWWgo4Nsx9M (Accessed: 2020-10-27).

315.

Phoenix Arizona Population and Demographics Resources. https://phoenix.areaconnect.com/statistics.htm (Accessed: 2020-10-05).

316.

Pinot, R. Minimum spanning tree release under differential privacy constraints. arXiv preprint arXiv:1801.06423 (2018).

317.

Pinot, R., Yger, F., Gouy-Pailler, C. & Atif, J. A unified view on differential privacy and robustness to adversarial examples. Workshop on Machine Learning for CyberSecurity at ECMLPKDD 2019 (2019).

318.

PostgreSQL extension extension adding HyperLogLog data structures as a native data type. https://github.com/citusdata/postgresql-hll (Accessed: 2018-05-03).

319.

Poulson, J. Reports of a Silicon Valley/Military Divide Have Been Greatly Exaggerated. Tech Inquiry. (2020).

320.

Privacy on Beam. https://github.com/google/differential-privacy/tree/main/privacy-on-beam (Accessed: 2020-09-23).

321.

Project, H. P. T. Randomness and Noise. https://github.com/opendifferentialprivacy/whitenoise-core/blob/develop/whitepapers/noise/noise.pdf (Accessed: 2020-08-18).

322.

Proserpio, D., Goldberg, S. & McSherry, F. Calibrating data to sensitivity in private data analysis: a platform for differentially-private analysis of weighted datasets. Proceedings of the VLDB Endowment (2014).

323.

Protocol Buffers. https://developers.google.com/protocol-buffers/ (Accessed: 2020-09-08).

324.

Pyrgelis, A., Troncoso, C. & Cristofaro, E. D. Knock Knock, Who’s There? Membership Inference on Aggregate Location Data. In: Proceedings of the 25th Network and Distributed System Security Symposium (NDSS 2018). Internet Society: San Diego, CA, USA. (2018) (2018).

325.

random() in random.py — Python 3.4 Source Code. https://github.com/python/cpython/blob/05c28b08f6e2fc8782472b026c98a3fdd61a2ba9/Lib/random.py%5C#L649 (Accessed: 2020-10-16).

326.

Rastogi, V., Hay, M., Miklau, G. & Suciu, D. Relationship privacy: output perturbation for queries with joins. Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2009).

327.

Rebollo-Monedero, D., Forne, J. & Domingo-Ferrer, J. From t-closeness-like privacy to postrandomization via information theory. IEEE Transactions on Knowledge and Data Engineering (2010).

328.

Rebollo-Monedero, D. & Forné, J. Optimized query forgery for private information retrieval. IEEE Transactions on Information Theory (2010).

329.

Reuben, J. Towards a Differential Privacy Theory for Edge-Labeled Directed Graphs. SICHERHEIT 2018 (2018).

330.

Reviriego, P. & Ting, D. Security of HyperLogLog (HLL) Cardinality Estimation: Vulnerabilities and Protection. IEEE Communications Letters vol. 24(5), p. 976 (2020).

331.

Rocher, L., Hendrickx, J. M. & De Montjoye, Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nature communications vol. 10(1), p. 1 (2019).

332.

Rogers, R., Subramaniam, S., Peng, S., Durfee, D., Lee, S., Kancha, S. K., Sahay, S. & Ahammad, P. LinkedIn’s Audience Engagements API: A privacy preserving data analytics system at scale. arXiv preprint arXiv:2002.05839 (2020).

333.

Roth, A. New algorithms for preserving differential privacy. Microsoft Research (2010).

334.

Roy, I., Setty, S. T., Kilzer, A., Shmatikov, V. & Witchel, E. Airavat: Security and privacy for MapReduce. NSDI. vol. 10 (2010), p. 297.

335.

Rubinstein, B. I. & Aldà, F. Pain-free random differential privacy with sensitivity sampling. Proceedings of the 34th International Conference on Machine Learning-Volume 70 (2017).

336.

Sablayrolles, A., Matthijs, D., Schmid, C., Ollivier, Y. & Jegou, H. White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. ICML 2019 : Thirty-sixth International Conference on Machine Learning. vol. 97 (2019), p. 5558.

337.

Samarati, P. Protecting respondents identities in microdata release. IEEE transactions on Knowledge and Data Engineering (2001).

338.

Samarati, P. & Sweeney, L. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Tech. rep. (technical report, SRI International, 1998).

339.

sdcMicro: Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation. https://CRAN.R-project.org/package=sdcMicro (Accessed: 2020-10-05).

340.

Sealfon, A. Shortest paths and distances with differential privacy. Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (2016).

341.

Semantic types of protocol buffer fields can be annotated using custom options. https://developers.google.com/protocol-buffers/docs/proto%5C#options (Accessed: 2020-09-08).

342.

Sen, S., Guha, S., Datta, A., Rajamani, S. K., Tsai, J. & Wing, J. M. Bootstrapping privacy compliance in big data systems. 2014 IEEE Symposium on Security and Privacy (2014), p. 327.

343.

Serwer, A. A Crime by Any Name. The Atlantic. (2019).

344.

Shi, E., Chan, H., Rieffel, E., Chow, R. & Song, D. Privacy-preserving aggregation of time-series data. Annual Network & Distributed System Security Symposium (NDSS) (2011).

345.

Shokri, R., Stronati, M., Song, C. & Shmatikov, V. Membership inference attacks against machine learning models. 2017 IEEE Symposium on Security and Privacy (SP) (2017), p. 3.

346.

Shukla, A., Deshpande, P., Naughton, J. F. & Ramasamy, K. Storage estimation for multidimensional aggregates in the presence of hierarchies. VLDB (1996).

347.

Simmons, S., Sahinalp, C. & Berger, B. Enabling privacy-preserving GWASs in heterogeneous human populations. Cell systems (2016).

348.

Sommer, D. M., Meiser, S. & Mohammadi, E. Privacy loss classes: The central limit theorem in differential privacy. Proceedings on Privacy Enhancing Technologies (2019).

349.

Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D. & Megías, D. Individual differential privacy: A utility-preserving formulation of differential privacy guarantees. IEEE Transactions on Information Forensics and Security (2017).

350.

Steinke, T. & Ullman, J. The Pitfalls of Average-Case Differential Privacy. https://differentialprivacy.org/average-case-dp/ (Accessed: 2020-08-14).

351.

Sun, H., Xiao, X., Khalil, I., Yang, Y., Qin, Z., Wang, H. W. & Yu, T. Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (2019).

352.

Sweeney, L. Computational disclosure control: a primer on data privacy protection. PhD thesis (Massachusetts Institute of Technology, 2001).

353.

Sweeney, L. Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (2002).

354.

Sweeney, L. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (2002).

355.

Sweeney, L. Only you, your doctor, and many others may know. Technology Science vol. 2015092903(9), p. 29 (2015).

356.

Szymkow, B. Roadmap to Differential Privacy for All. https://blog.openmined.org/making-algorithms-private/ (Accessed: 2020-08-18).

357.

Takagi, S., Cao, Y., Asano, Y. & Yoshikawa, M. Geo-Graph-Indistinguishability: Protecting Location Privacy for LBS over Road Networks. IFIP Annual Conference on Data and Applications Security and Privacy (2019).

358.

Tang, J., Korolova, A., Bai, X., Wang, X. & Wang, X. Privacy loss in apple’s implementation of differential privacy on macos 10.12. arXiv preprint arXiv:1709.02753 (2017).

359.

Task, C. & Clifton, C. A guide to differential privacy theory in social network analysis. Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) (2012).

360.

Team, D. P. Learning with Privacy at Scale (2017).

361.

Team, G. D. P. Secure Noise Generation. https://github.com/google/differential-privacy/blob/main/common_docs/Secure_Noise_Generation.pdf (Accessed: 2020-09-25).

362.

The Effects of Climate Change. https://climate.nasa.gov/effects/ (Accessed: 2020-09-09).

363.

The SECRETA system. http://users.uop.gr/~poulis/SECRETA/index.html (Accessed: 2020-10-05).

364.

Toledo, R. R., Danezis, G. & Goldberg, I. Lower-cost e-private information retrieval. Proceedings on Privacy Enhancing Technologies (2016).

365.

Tolerance Calculation for ApproxEquals in Tests — Privacy on Beam. https://github.com/google/differential-privacy/blob/main/privacy-on-beam/docs/Tolerance_Calculation.pdf (Accessed: 2020-10-16).

366.

Tossou, A. C. & Dimitrakakis, C. Algorithms for differentially private multi-armed bandits. Thirtieth AAAI Conference on Artificial Intelligence (2016).

367.

Triastcyn, A. & Faltings, B. Bayesian Differential Privacy for Machine Learning. ICML 2020: 37th International Conference on Machine Learning (2020).

368.

Tschantz, M. C., Sen, S. & Datta, A. SoK: Differential Privacy as a Causal Property. 2020 IEEE Symposium on Security and Privacy (SP) (2020).

369.

Tschorsch, F. & Scheuermann, B. An algorithm for privacy-preserving distributed user statistics. Computer Networks (2013).

370.

Tumult Labs. https://www.tmlt.io/ (Accessed: 2020-08-18).

371.

UTD Anonymization Toolbox. http://cs.utdallas.edu/dspl/cgi-bin/toolbox/index.php (Accessed: 2020-10-05).

372.

Vadhan, S. Tutorials on the Foundations of Cryptography, p. 347 (Springer, 2017).

373.

Venkatadri, G., Andreou, A., Liu, Y., Mislove, A., Gummadi, K. P., Loiseau, P. & Goga, O. Privacy Risks with Facebook’s PII-Based Targeting: Auditing a Data Broker’s Advertising Interface (2018), p. 89.

374.

Von Voigt, S. N. & Tschorsch, F. RRTxFM: Probabilistic Counting for Differentially Private Statistics. Conference on e-Business, e-Services and e-Society (2019), p. 86.

375.

Wagh, S., Cuff, P. & Mittal, P. Differentially private oblivious ram. Proceedings on Privacy Enhancing Technologies (2018).

376.

Wagner, I. & Eckhoff, D. Technical privacy metrics: a systematic survey. ACM Computing Surveys (CSUR) (2018).

377.

Waldman, P., Chapman, L. & Robertson, J. Palantir knows everything about you. Bloomberg. (2018).

378.

Wang, W., Ying, L. & Zhang, J. On the relation between identifiability, differential privacy, and mutual-information privacy. IEEE Transactions on Information Theory (2016).

379.

Wang, Y.-X. Per-instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression. arXiv preprint arXiv:1707.07708 (2017).

380.

Wang, Y.-X., Balle, B. & Kasiviswanathan, S. P. Subsampled Rényi Differential Privacy and Analytical Moments Accountant. The 22nd International Conference on Artificial Intelligence and Statistics (2019), p. 1226.

381.

Wang, Y.-X., Lei, J. & Fienberg, S. E. On-average kl-privacy and its equivalence to generalization for max-entropy mechanisms. International Conference on Privacy in Statistical Databases (2016).

382.

Wang, Y., Sibai, H., Mitra, S. & Dullerud, G. E. Differential Privacy for Sequential Algorithms. arXiv preprint arXiv:2004.00275 (2020).

383.

Warner, S. L. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association (1965).

384.

Wasserman, L. All of statistics: a concise course in statistical inference (Springer Science & Business Media, 2013).

385.

Wasserman, L. & Zhou, S. A statistical framework for differential privacy. Journal of the American Statistical Association (2010).

386.

Weinstein, A. This is Fascism. The New Republic. (2020).

387.

Whang, K.-Y., Vander-Zanden, B. T. & Taylor, H. M. A linear-time probabilistic counting algorithm for database applications. ACM Transactions on Database Systems (TODS) (1990).

388.

Wilson, R. J., Zhang, C. Y., Lam, W., Desfontaines, D., Simmons-Marengo, D. & Gipson, B. Differentially Private SQL with Bounded User Contribution. Proceedings on Privacy Enhancing Technologies vol. 2020(2) (2020).

389.

Wong, R. C.-W., Fu, A. W.-C., Wang, K. & Pei, J. Anonymization-based attacks in privacy-preserving data publishing. ACM Transactions on Database Systems (TODS) vol. 34(2), p. 1 (2009).

390.

Wu, G., He, Y., Wu, J. & Xia, X. Inherit differential privacy in distributed setting: Multiparty randomized function computation. Trustcom/BigDataSE/I SPA, 2016 IEEE (2016).

391.

Wu, G., Xia, X. & He, Y. Information Theory of Data Privacy. arXiv preprint arXiv:1703.07474 (2017).

392.

Wu, X., Dou, W. & Ni, Q. Game theory based privacy preserving analysis in correlated data publication. Proceedings of the Australasian Computer Science Week Multiconference (2017).

393.

Wu, X., Wu, T., Khan, M., Ni, Q. & Dou, W. Game theory based correlated privacy preserving analysis in big data. IEEE Transactions on Big Data (2017).

394.

Xiao, Q., Zhou, Y. & Chen, S. Better with fewer bits: Improving the performance of cardinality estimation of large data streams. IEEE INFOCOM 2017-IEEE Conference on Computer Communications (2017), p. 1.

395.

Xiao, X. & Tao, Y. M-invariance: towards privacy preserving re-publication of dynamic datasets. Proceedings of the 2007 ACM SIGMOD international conference on Management of data (2007), p. 689.

396.

Xiao, Y. & Xiong, L. Protecting locations with differential privacy under temporal correlations. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015).

397.

Xu, F., Tu, Z., Li, Y., Zhang, P., Fu, X. & Jin, D. Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data. Proceedings of the 26th international conference on world wide web (2017), p. 1241.

398.

Yan, Z., Liu, J., Li, G., Han, Z. & Qiu, S. PrivMin: Differentially Private MinHash for Jaccard Similarity Computation. arXiv preprint arXiv:1705.07258 (2017).

399.

Yang, B., Sato, I. & Nakagawa, H. Bayesian differential privacy on correlated data. Proceedings of the 2015 ACM SIGMOD international conference on Management of Data (2015).

400.

Yao, C., Wang, X. S. & Jajodia, S. Checking for k-anonymity violation by views. Proceedings of the 31st international conference on Very large data bases (2005), p. 910.

401.

Ying, X., Wu, X. & Wang, Y. On linear refinement of differential privacy-preserving query answering. Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013).

402.

Yu, Y. W. & Weber, G. M. HyperMinHash: MinHash in LogLog space. arXiv preprint arXiv:1710.08436 (2017).

403.

Zenz, A. ‘Thoroughly reforming them towards a healthy heart attitude’: China’s political re-education campaign in Xinjiang. Central Asian Survey vol. 38(1), p. 102 (2019).

404.

Zhang, D., McKenna, R., Kotsogiannis, I., Hay, M., Machanavajjhala, A. & Miklau, G. Ektelo: A framework for defining differentially-private computations. Proceedings of the 2018 International Conference on Management of Data (2018), p. 115.

405.

Zhang, J., Sun, J., Zhang, R., Zhang, Y. & Hu, X. Privacy-Preserving Social Media Data Outsourcing. IEEE INFOCOM 2018-IEEE Conference on Computer Communications (2018).

406.

Zhang, L., Jajodia, S. & Brodsky, A. Information disclosure under realistic assumptions: Privacy versus optimality. Proceedings of the 14th ACM conference on Computer and communications security (2007), p. 573.

407.

Zhang, Z., Qin, Z., Zhu, L., Jiang, W., Xu, C. & Ren, K. Toward practical differential privacy in smart grid with capacity-limited rechargeable batteries. 2015.

408.

Zhao, J., Wang, T., Bai, T., Lam, K.-Y., Ren, X., Yang, X., Shi, S., Liu, Y. & Yu, H. Reviewing and improving the Gaussian mechanism for differential privacy. arXiv preprint arXiv:1911.12060 (2019).

409.

Zhou, S., Ligett, K. & Wasserman, L. Differential privacy with compression. Information Theory, 2009. ISIT 2009. IEEE International Symposium on (2009).

410.

Zhu, T., Li, G., Ren, Y., Zhou, W. & Xiong, P. Differential privacy for neighborhood-based collaborative filtering. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2013).

411.

Zhu, T., Xiong, P., Li, G. & Zhou, W. Correlated differential privacy: hiding information in non-IID data set. IEEE Transactions on Information Forensics and Security (2015).

412.

Zhu, X. & Ghahramani, Z. Learning from labeled and unlabeled data with label propagation. Tech. Rep., Technical Report CMU-CALD-02–107, Carnegie Mellon University (2002).

All opinions here are my own, not my employers.
I'm always glad to get feedback! If you'd like to contact me, please do so via e-mail (se.niatnofsed@neimad) or Twitter (@TedOnPrivacy).