Last update: 2025-07-08

Hello!

My name is Damien Desfontaines — also known as Ted, or any pseudonym recognized by the (Ted)+ regular expression — and this is the sober version of my personal webpage. The fancier one, which is now quite outdated, can be found here.

About me

I'm the founder of Hiding Nemo, an independent consultancy helping organizations do more with data in a responsible way, using privacy-enhancing technology.

Before that, I led the anonymization consulting team at Google, and worked as a staff scientist at Tumult Labs. In both jobs, I focused on making it easier to deploy differential privacy for practical applications.

In December 2020, I defended my PhD thesis at ETH Zürich, which I worked on part-time in the Information Security Group, under the supervision of David Basin.

Previously, I studied at the École normale supérieure, where I completed a Master's degree in mathematical logic and theoretical computer science called the LMFI.

You can find more information on my LinkedIn profile. I'm also on Mastodon and Bluesky.

Research

When I was studying for my Master's degree, I did research in theoretical computer science, and more specifically computability theory.

• 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)
  tl;dr: 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, 2016 (PDF)
  tl;dr: longer version of the previous paper.

Since then, I've been working on data anonymization, and more specifically differential privacy.

• 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.
• Damien Desfontaines, Esfandiar Mohammadi, David Basin, Elisabeth Krahmer
  Differential privacy with partial knowledge
  arXiv preprint, 2019 (PDF, blog post)
  tl;dr: what happens to differential privacy when modeling an attacker with only partial knowledge over the data.
• 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: an SQL-based system to anonymize data easily and securely using differential privacy.
• Damien Desfontaines, Balázs Pejó
  SoK: Differential Privacies
  PETS, 2020 (PDF, recording of my talk, book)
  tl;dr: a categorized list of all variants and extensions of differential privacy
• Shruthi Gorantala, Rob Springer, Sean Purser-Haskell, William Lam, Royce Wilson, Asra Ali, Eric P. Astor, Itai Zukerman, Sam Ruth, Christoph Dibak, Phillipp Schoppmann, Sasha Kulankhina, Alain Forget, David Marn, Cameron Tew, Rafael Misoczki, Bernat Guillen, Xinyu Ye, Dennis Kraft, Damien Desfontaines, Aishe Krishnamurthy, Miguel Guevara, Milinda Perera, Yurii Sushko, Bryant Gipson
  A General Purpose Transpiler for Fully Homomorphic Encryption
  arXiv preprint, 2021 (PDF, recording of Shruthi and Rob's talk for FHE.org)
  tl;dr: a system to make it easier to write software that operates on encrypted data.
• 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 social distancing policies on mobility and COVID-19 case growth in the US.
  Nature Communications, 2021 (HTML)
  tl;dr: analyzing the efficacy of social distancing measures during the COVID crisis, using anonymized data from Google Location History users.
• Damien Desfontaines, James Voss, Bryant Gipson, Chinmoy Mandayam
  Differentially private partition selection
  PETS, 2022 (PDF, poster for TPDP 2020)
  tl;dr: an optimal technique to publish differentially private statistics on an unknown domain, when each person contributes to a single group.
• Samuel Haney, Damien Desfontaines, Luke Hartman, Ruchit Shrestha, Michael Hay
  Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers
  arXie preprint, 2022 (PDF, blog post, poster for TPDP 2022)
  tl;dr: we find vulnerabilities in the way open-source differential privacy libraries handle floating-point numbers, and propose a solution.
• Skye Berghel, Philip Bohannon, Damien Desfontaines, Charles Estes, Sam Haney, Luke Hartman, Michael Hay, Ashwin Machanavajjhala, Tom Magerlein, Gerome Miklau, Amritha Pai, William Sexton, Ruchit Shrestha
  Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
  arXiv preprint, 2022 (PDF, recording of Michael's talk at PEPR '22)
  tl;dr: a high-level description of a system to build complex differential privacy programs, without sacrificing safety, usability, or performance.
• Marika Swanberg, Damien Desfontaines, Samuel Haney
  DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection
  PETS, 2023 (PDF)
  tl:dr: a performant and scalable technique for differential privacy on unknown domains, when each person contributes to many groups.

I serve as a program committee member for PETS (since 2020), PEPR (since 2022), and TPDP (since 2025); I was also in the program committee of PPAI in 2023. If you'd like to invite me to volunteer for another scientific conference or journal, please consult this blog post first.

Other publications, talks, interviews, etc.

• I have a blog, where I mostly write about anonymization in simple terms. I recommend starting with my friendly introduction to differential privacy, for which I've gotten enthusiastic feedback from a number of folks.
• My PhD thesis is available online as a series of Web pages. Its index highlights the sections that don't have too much math in them, in an effort to make parts of it accessible for people who aren't experts.
• I used to contribute to Google's differential privacy libraries, parts of which are open-source. Now, I help maintain Tumult Analytics, an open-source library for differential privacy built by my colleagues and I at Tumult Labs, and transferred to OpenDP once Tumult Labs got acquired by LinkedIn. Check out our tutorial series!
• 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 three of these projects, we published a technical description of the anonymization process:
  • the Community Mobility Reports (PDF), which won the Award for Responsible Data Stewardship from the Future of Privacy Forum,
  • the Symptom Search Trends (PDF),
  • and the Vaccination Search Insights (PDF).
• Miguel Guevara and I were interviewed by the ACM to discuss our efforts around differential privacy at Google. The interview (PDF) was published in ACM Queue and Communications of the ACM.
• I co-authored a blog post titled Statistical Inference is Not a Privacy Violation with other differential privacy experts. In it, we argue that certain criticisms of differential privacy are based on a misunderstanding of what constitutes a successful privacy attack on aggregated data.
• I delivered an invited talk at PPAI-22 about making differential privacy a standard practice in the industry. The slides and the transcript of this talk are available on my blog.
• I gave a talk about differential privacy and a case study for its use at the Internal Revenue Service for the ISSA Privacy SIG. The original recording is only available to ISSA members, but I published another recording of this talk afterwards.
• Debra Farber invited me to be a guest on her Shifting Privacy Left podcast. In the interview, I talk about differential privacy, and Tumult Labs' approach to deploy it to solve real-world use cases.
• Katharina Koerner invited me to participate to an IAPP panel with June Brawner and Nigel Smart. Our conversation touches on various privacy-enhancing technologies, how they relate to each other, and where they are headed.
• Katharine Jarmul hired me as one of the scientific consultants and editors for her excellent book about privacy-enhancing technologies, Practical Data Privacy.
• I co-authored a position paper titled Challenges towards the Next Frontier in Privacy (PDF) with many other differential privacy experts. In this paper, we describe the current state of the field, and provide guidance on key future research directions.
• Katharine Jarmul interviewed me for her Provably Private YouTube series; in the video, I talk about privacy engineering, my own path to privacy and anonymization, and how I expect the field to evolve over time.

Contact

To get in touch with me, you can send an e-mail to se.niatnofsed@neimad (beware when copying it). I also use WhatsApp and Signal, feel free to ask me for my number if that's your favorite communication method.

I also love to send and receive postcards. If that's also your thing, don't hesitate to ask me for my mailing address ♥