Dispatch: secure, resilient mobile reporting. - Kanak Biscuitwala; Willem Bult; Mathias Lécuyer; T. J. Purtell; Madeline K. B. Ross; Augustin Chaintreau; Chris Haseman; Monica S. Lam; Susan E. McGregor (SIGCOMM, 2013)

Weaving a safe web of news. - Kanak Biscuitwala; Willem Bult; Mathias Lécuyer; T. J. Purtell; Madeline K. B. Ross; Augustin Chaintreau; Chris Haseman; Monica S. Lam; Susan E. McGregor (WWW (Companion Volume), 2013)

XRay: Enhancing the Web's Transparency with Differential Correlation. - Mathias Lécuyer; Guillaume Ducoffe; Francis Lan; Andrei Papancea; Theofilos Petsios; Riley Spahn; Augustin Chaintreau; Roxana Geambasu (USENIX Security Symposium, 2014)

Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence. - Mathias Lécuyer; Riley Spahn; Yannis Spiliopolous; Augustin Chaintreau; Roxana Geambasu; Daniel J. Hsu (CCS, 2015)

Synapse: a microservices architecture for heterogeneous-database web applications. - Nicolas Viennot; Mathias Lécuyer; Jonathan Bell 001; Roxana Geambasu; Jason Nieh (EuroSys, 2015)

Web Transparency for Complex Targeting: Algorithms, Limits, and Tradeoffs. - Guillaume Ducoffe; Mathias Lécuyer; Augustin Chaintreau; Roxana Geambasu (SIGMETRICS, 2015)

Harvesting Randomness to Optimize Distributed Systems. - Mathias Lécuyer; Joshua Lockerman; Lamont Nelson; Siddhartha Sen 001; Amit Sharma 007; Aleksandrs Slivkins (HotNets, 2017)

Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization. - Mathias Lécuyer; Riley Spahn; Roxana Geambasu; Tzu-Kuo Huang; Siddhartha Sen 001 (IEEE Symposium on Security and Privacy, 2017)

Improving the Transparency of the Sharing Economy. - Mathias Lécuyer; Max Tucker; Augustin Chaintreau (WWW (Companion Volume), 2017)

On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning. - Mathias Lécuyer; Vaggelis Atlidakis; Roxana Geambasu; Daniel Hsu 001; Suman Jana (CoRR, 2018)

Enhancing Selectivity in Big Data. - Mathias Lécuyer; Riley Spahn; Roxana Geambasu; Tzu-Kuo Huang; Siddhartha Sen 001 (IEEE Secur. Priv., 2018)

Certified Robustness to Adversarial Examples with Differential Privacy. - Mathias Lécuyer; Vaggelis Atlidakis; Roxana Geambasu; Daniel Hsu 001; Suman Jana (IEEE Symposium on Security and Privacy, 2019)

Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform. - Mathias Lécuyer; Riley Spahn; Kiran Vodrahalli; Roxana Geambasu; Daniel Hsu 001 (ACM SIGOPS Oper. Syst. Rev., 2019)

Security, Privacy, and Transparency Guarantees for Machine Learning Systems. - Mathias Lécuyer (2019)

Sayer: Using Implicit Feedback to Optimize System Policies. - Mathias Lécuyer; Sang Hoon Kim; Mihir Nanavati; Junchen Jiang; Siddhartha Sen 001; Aleksandrs Slivkins; Amit Sharma 007 (SoCC, 2021)

Privacy Budget Scheduling. - Tao Luo; Mingen Pan; Pierre Tholoniat; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer (OSDI, 2021)

Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning. - Mathias Lécuyer (CoRR, 2021)

Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. - Ali Behrouz; Mathias Lécuyer; Cynthia Rudin; Mango I. Seltzer (CIKM Workshops, 2022)

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments. - Jinkun Lin; Anqi Zhang; Mathias Lécuyer; Jinyang Li 001; Aurojit Panda; Siddhartha Sen 001 (ICML, 2022)

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. - Ali Behrouz; Mathias Lécuyer; Cynthia Rudin; Margo I. Seltzer (CoRR, 2022)

Packing Privacy Budget Efficiently. - Pierre Tholoniat; Kelly Kostopoulou; Mosharaf Chowdhury; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer; Junfeng Yang (CoRR, 2022)

GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning. - Shiqi He; Qifan Yan; Feijie Wu; Lanjun Wang; Mathias Lécuyer; Ivan Beschastnikh (MLSys, 2023)

Turbo: Effective Caching in Differentially-Private Databases. - Kelly Kostopoulou; Pierre Tholoniat; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer (SOSP, 2023)

DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation. - Qiaoyue Tang; Mathias Lécuyer (CoRR, 2023)

Boost: Effective Caching in Differentially-Private Databases. - Kelly Kostopoulou; Pierre Tholoniat; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer (CoRR, 2023)

DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction). - Qiaoyue Tang; Frederick Shpilevskiy; Mathias Lécuyer (AAAI, 2024)

PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining. - Mishaal Kazmi; Hadrien Lautraite; Alireza Akbari; Qiaoyue Tang; Mauricio Soroco; Tao Wang; Sébastien Gambs; Mathias Lécuyer (NeurIPS, 2024)

Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences. - Saiyue Lyu; Shadab Shaikh; Frederick Shpilevskiy; Evan Shelhamer; Mathias Lécuyer (NeurIPS, 2024)

Cookie Monster: Efficient On-Device Budgeting for Differentially-Private Ad-Measurement Systems. - Pierre Tholoniat; Kelly Kostopoulou; Peter McNeely; Prabhpreet Singh Sodhi; Anirudh Varanasi; Benjamin Case; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer (SOSP, 2024)

NetShaper: A Differentially Private Network Side-Channel Mitigation System. - Amir Sabzi; Rut Vora; Swati Goswami; Margo I. Seltzer; Mathias Lécuyer; Aastha Mehta (USENIX Security Symposium, 2024)

Efficient and Adaptive Posterior Sampling Algorithms for Bandits. - Bingshan Hu; Zhiming Huang 002; Tianyue H. Zhang; Mathias Lécuyer; Nidhi Hegde 001 (CoRR, 2024)

Alistair: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems. - Pierre Tholoniat; Kelly Kostopoulou; Peter McNeely; Prabhpreet Singh Sodhi; Anirudh Varanasi; Benjamin Case; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer (CoRR, 2024)

Adaptive Randomized Smoothing: Certifying Multi-Step Defences against Adversarial Examples. - Saiyue Lyu; Shadab Shaikh; Frederick Shpilevskiy; Evan Shelhamer; Mathias Lécuyer (CoRR, 2024)

Training and Evaluating Causal Forecasting Models for Time-Series. - Thomas Crasson; Yacine Nabet; Mathias Lécuyer (CoRR, 2024)

DPack: Efficiency-Oriented Privacy Budget Scheduling. - Pierre Tholoniat; Kelly Kostopoulou; Mosharaf Chowdhury; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer; Junfeng Yang (EuroSys, 2025)

FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching. - Qifan Yan; Andrew Liu; Shiqi He; Mathias Lécuyer; Ivan Beschastnikh (INFOCOM, 2025)

Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret. - Bingshan Hu; Zhiming Huang 002; Tianyue H. Zhang; Mathias Lécuyer; Nidhi Hegde 001 (CoRR, 2025)

Big Bird: Privacy Budget Management for W3C's Privacy-Preserving Attribution API. - Pierre Tholoniat; Alison Caulfield; Giorgio Cavicchioli; Mark Chen; Nikos Goutzoulias; Benjamin Case; Asaf Cidon; Roxana Geambasu; Mathias Lécuyer; Martin Thomson (CoRR, 2025)

Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion. - Frederick Shpilevskiy; Saiyue Lyu; Krishnamurthy Dj Dvijotham; Mathias Lécuyer; Pierre-André Noël (CoRR, 2025)

On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance. - Qiaoyue Tang; Alain Zhiyanov; Mathias Lécuyer (CoRR, 2025)