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 The fancier one, which is now quite outdated, can be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:
- 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 ♥