Pierre Tholoniat
Ph.D. Student in Computer Science, Columbia University

I am a Ph.D. student in computer science at Columbia University, working with Asaf Cidon and Roxana Geambasu. I did my undergrad in mathematics and computer science at Ecole Polytechnique, in France.

I am researching security and privacy, with a focus on differential privacy, applied cryptography and distributed systems. I enjoy solving socially meaningful problems by building practical systems that rely on sound theoretical foundations. Lately, I have been working on systems for differential privacy, applied to privacy-preserving machine learning and analytics. I enjoy contributing to open-source projects.

Feel free to contact me at pierre@cs.columbia.edu. Here is my PGP public key. I’m also on Matrix and Signal. My name is pronounced [pjɛʁ to.lo.ɲa], which sounds like “pee-air to-lo-nee-ah”.


For more details, please see my LinkedIn or my 2-page resume (PDF).


  • Columbia University. M.S. & Ph.D. in Computer Science (2019 – current). Advisor: Roxana Geambasu. New York, NY.

  • École Polytechnique. B.S. & M.S. in Engineering (Grande Ecole master program, 2016 – 2019). Palaiseau, FR.

  • Université Paris Nanterre. B.S. in Philosophy (2017 – 2018). Nanterre, FR.

  • Lycée Sainte-Geneviève. Prépa in Mathematics and Physics (2014 – 2016). Versailles, FR.


  • Columbia University. Graduate Research Assistant (2021 - current). New York, NY.

  • Cloudflare. Research Intern (2023). Boston, MA.

  • Microsoft Research. Research Intern (2022). Redmond, WA.

  • École Normale Supérieure. Research Intern (2020). Paris, FR.

  • The University of Sydney. Visiting Researcher (2019). Sydney, AU.

  • Muvee Technologies. Software Engineering Intern (2018). Singapore, SG.

  • French Armed Forces. Officer cadet (2016 – 2017). Tahiti, PF.


Ongoing projects

Here are my main projects:

  • Privacy as a resource. At Columbia, I am designing orchestration systems and scheduling algorithms for differential privacy. We implemented PrivateKube (published at OSDI'21), a system to manage privacy budget as a resource (akin to CPU or RAM) on Kubernetes clusters.

Some past projects

See my publications below for more details.

  • Large-scale private systems. I collaborated with a team at Brookhaven National Laboratory that designs and evaluates large scale privacy-preserving machine learning systems running on top of the U.S. Department of Energy’s supercomputers. We improved support for distributed training in PyTorch’s differential privacy library, Opacus.

  • Function secret sharing for encrypted deep learning. I am maintaining an open-source library for OpenMined. Sycret is the first Python library for function secret sharing. It relies on an efficient parallelized Rust backend using cryptographic hardware acceleration. We plugged it into PySyft, one of the leading privacy-preserving machine learning frameworks, to build ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing (published at PETS'22).

  • Distributed algorithms. I spent 5 months in 2019 at the Concurrent Systems Research Group of the University of Sydney, where I studied cross-chain protocols and the formal verification of distributed algorithms with Vincent Gramoli and Rob van Glabbeek. In 2020 we worked on a compositional approach to model-check consensus algorithms. Our research appeared at SPAA'20, PODC'22 and DISC'22.

  • Undergraduate research. Among other things, I worked on code-based proofs with EasyCrypt at Inria and designed a crypto-economic system that made it to the Blockchain Innovation Challenge.


Please see Semantic Scholar or Google Scholar for a more detailed list with preprints and technical reports.

Peer-reviewed conference and journal proceedings

T. Ryffel, P. Tholoniat, D. Pointcheval, and F. Bach, AriaNN: Low-interaction privacy-preserving deep learning via function secret sharing, in Proceedings on Privacy Enhancing Technologies (PETS 22), 2022, doi: 10.2478/popets-2022-0015.

T. Luo, M. Pan, P. Tholoniat, A. Cidon, R. Geambasu, and M. Lécuyer, Privacy budget scheduling, in 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21), 2021, https://www.usenix.org/conference/osdi21/presentation/luo.

N. Bertrand, V. Gramoli, M. Lazić, I. Konnov, P. Tholoniat, J. Widder, Holistic Verification of Blockchain Consensus, in 36th International Symposium on Distributed Computing (DISC 22), 2022, doi: 10.4230/LIPIcs.DISC.2022.10. A short version also appeared at PODC'22 (doi: 10.1145/3519270.3538468).

R. van Glabbeek, V. Gramoli, and P. Tholoniat, Feasibility of Cross-Chain Payment with Success Guarantees, in Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 20), 2020, doi: 10.1145/3350755.3400264. An extended version was published in Distrib. Comput., vol. 36, no. 2, pp. 137–157, Jun. 2023, issn: 1432-0452. doi: 10.1007/s00446-023-00446-0.

Book chapters

P. Tholoniat and V. Gramoli, Formal verification of blockchain byzantine fault tolerance, in Handbook on Blockchain. Springer Optimization and Its Applications, 2022. doi: 10.1007/978-3-031-07535-3_12