Hello!

Scientific papers

When I was studying for my Master's degree, I worked on pretty theoretical stuff.

• Cristian S. Calude, Damien Desfontaines.
  Universality and Almost Decidability.
  Fundamenta Informaticae, 2015 (PDF, tl;dr: maybe it's possible to approximate the halting problem!)
• Cristian S. Calude, Damien Desfontaines.
  Anytime Algorithms for Non-Ending Computations.
  IJFCS, 2015 (PDF, longer version of the previous paper)
• Laurent Bienvenu, Damien Desfontaines, Alexander Shen.
  What Percentage of Programs Halt?.
  ICALP, 2015 (PDF, tl;dr: actually, no, it's impossible.)
• Laurent Bienvenu, Damien Desfontaines, Alexander Shen.
  Generic algorithms for halting problem and optimal machines revisited.
  LMCS, 2015 (PDF, longer version of the previous paper)

Since them, both on my PhD time and on my engineering time, I've been working on data anonymization.

• Damien Desfontaines, Andreas Lochbihler, David Basin.
  Cardinality Estimators do not Preserve Privacy.
  PETS, 2019 (PDF, blog post, recording of my talk, tl;dr: when counting people, in order to remember who you've already counted, you have to remember who you've already counted.)
  ✨ Best Student Paper Award ✨
• Pern Hui Chia, Damien Desfontaines, Milinda Perera, Daniel Simmons-Marengo, Chao Li, Wei-Yen Day, Qiushi Wang, Miguel Guevara.
  KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale.
  IEEE S&P, 2019 (PDF, recording of Pern's talk, tl;dr: a clever way of estimating how easy it is to reidentify people in very large datasets.)
• Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, Bryant Gipson.
  Differentially Private SQL with Bounded User Contribution.
  PETS, 2020 (PDF, recording of Royce's talk, tl;dr: a system to anonymize data easily and securely.)
• Damien Desfontaines, Balázs Pejó.
  SoK: Differential Privacies.
  PETS, 2020 (PDF, recording of my talk, tl;dr: a categorized list of all variants and extensions of differential privacy, a central concept in anonymization.)
• Damien Desfontaines, Esfandiar Mohammadi, David Basin, Elisabeth Krahmer.
  Differential privacy with partial knowledge.
  Under review (PDF, tl;dr: what happens to differential privacy when modeling an attacker with only partial knowledge over the data.)
• Damien Desfontaines, James Voss, Bryant Gipson, Chinmoy Mandayam.
  Differentially private partition selection.
  Under review (PDF, poster for TPDP 2020, tl;dr: an optimized technique to publish differentially private statistics on an unknown domain.)
• Gregory A Wellenius, Swapnil Vispute, Valeria Espinosa, Alex Fabrikant, Thomas C Tsai, Jonathan Hennessy, Brian Williams, Krishna Gadepalli, Adam Boulanger, Adam Pearce, Chaitanya Kamath, Arran Schlosberg, Catherine Bendebury, Charlotte Stanton, Shailesh Bavadekar, Christopher Pluntke, Damien Desfontaines, Benjamin Jacobson, Zan Armstrong, Bryant Gipson, Royce J Wilson, Andrew Widdowson, Katherine Chou, Andrew Oplinger, Tomer Shekel, Ashish K Jha, Evgeniy Gabrilovich. Impacts of US State-Level Social Distancing Policies on Population Mobility and COVID-19 Case Growth During the First Wave of the Pandemic.
  Under review (PDF, tl;dr: what the title says, based on anonmyized data from Google Location History users.)

Other publications

During the COVID-19 crisis, I helped several efforts to publish anonymized data from Google users to help researchers and public health authorities understand and combat the pandemic. For two of these projects, we published a technical description of the anonymization process: the Community Mobility Reports (PDF) and the Search Trends Symptom Dataset (PDF).

In 2020, I was interviewed by the ACM with my colleague Miguel Guevara to discuss our efforts around differential privacy. The interview (PDF) was published in ACM Queue and Communications of the ACM.

I have a blog where I mostly discuss anonymization in simple terms. I also published my PhD thesis online as a series of Web pages, marking down the sections that don't have too much math in them.

Finally, my team at Google publishes and maintains open-source differential privacy software, accessible on our GitHub repository.