Publications

Jonas Tai, KN Sengottuvelavan, P Whiting, NP Hoang (2025). IRBlock: A Large-Scale Measurement Study of the Great Firewall of Iran. USENIX Sec'25.

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Y Yan, W Huang, I Grishchenko, G Saileshwar, A Mehta, D Lie (2025). Relocate-Vote: Using Sparsity Information to Exploit Ciphertext Side-Channels. USENIX Sec'25.

B Hu, Z Huang, T Zhang, M Lécuyer, N Hegde (2025). Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret. ICML'25.

T Bilot, B Jiang, Z Li, N El Madhoun, K Al Agha, A Zouaoui, S Iqbal, X Han, T Pasquier (2025). Sometimes Simpler is Better: A Comprehensive Analysis of State-of-the-Art Provenance-Based Intrusion Detection Systems. USENIX Sec'25.

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P Gupta, A Moghimi, D Sisodraker, M Shahrad, A Mehta (2025). Growlithe: A Developer-Centric Compliance Tool for Serverless Applications. S&P'25.

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S Nourin, E Rye, K Bock, NP Hoang, D Levin (2025). Is Nobody There? Good! Globally Measuring Connection Tampering without Responsive Endhosts. S&P'25.

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P Tholoniat, K Kostopoulou, M Chowdhury, A Cidon, R Geambasu, M Lécuyer, J Yang (2025). DPack: Efficiency-Oriented Privacy Budget Scheduling. EuroSys'25.

B Jiang, T Bilot, N El Madhoun, K Al Agha, A Zouaoui, S Iqbal, X Han, T Pasquier (2025). ORTHRUS: Achieving High Quality of Attribution in Provenance-based Intrusion Detection Systems. USENIX Sec'25.

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T Abrar, A Shamail, M J Iqbal, M Iqbal, A Zouaoui, A Ahmed, M Abdullah, M Shayan, F Zaffar, T Pasquier, D Eyers, A Gehani (2025). On the Reproducibility of Provenance-based Intrusion Detection that uses Deep Learning. ACM REP'25.

M Kazmi, H Lautraite, A Akbari, Q Tang, M Soroco, T Wang, S Gambs, M Lécuyer (2024). Panoramia: Privacy auditing of machine learning models without retraining. NeurIPS'24.

S Lyu, S Shaikh, F Shpilevskiy, E Shelhamer, M Lécuyer (2024). Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences. NeurIPS'24.

P Tholoniat, K Kostopoulou, P McNeely, P Sodhi, S Prabhpreet, A Varanasi, B Case, A Cidon, R Geambasu, M Lécuyer (2024). Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems. SOSP'24.

SY Lim, T Prasad, X Han, T Pasquier (2024). SafeBPF: Hardware-assisted Defense-in-depth for eBPF Kernel Extensions. ACM CCSW'24.

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A Sabzi, R Vora, S Goswami, M Seltzer, M Lécuyer, A Mehta (2024). NetShaper: A Differentially Private Network Side-Channel Mitigation System. USENIX Sec'24.

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NP Hoang, J Dalek, M Crete-Nishihata, N Christin, V Yegneswaran, M Polychronakis, N Feamster (2024). GFWeb: Measuring the Great Firewall's Web Censorship at Scale. USENIX Sec'24.

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J Tang, L Alvarez, A Brar, NP Hoang, N Christin (2024). Automatic Generation of Web Censorship Probe Lists. PoPETs ‘24.

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P Calle, L Savitsky, AN Bhagoji, NP Hoang, S Cho (2024). Toward Automated DNS Tampering Detection Using Machine Learning. FOCI ‘24.

Z Cheng, Q Lv, J Liang, Y Wang, D Sun, T Pasquier, X Han (2024). KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance. IEEE S&P'24.

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Q Tang, F Shpilevskiy, M Lécuyer (2024). Turbo: Effective Caching in Differentially-Private Databases. AAAI'24.

K Kostopoulou, P Tholoniat, A Cidon, R Geambasu, M Lécuyer (2023). Turbo: Effective Caching in Differentially-Private Databases. SOSP'23.

SY Lim, X Han, T Pasquier (2023). Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing. SIGCOMM Workshop on eBPF and Kernel Extensions.

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J Lin, A Zhang, M Lécuyer, J Li, A Panda, S Sen (2023). Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments. ICML'22.

Q Tang, M Lécuyer (2023). DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation. RTML'23.

A Mehta, M Alzayat, R D Viti, B B Brandenburg, P Druschel, D Garg (2022). Pacer: Comprehensive Network Side-Channel Mitigation in the Cloud. USENIX Sec'22.

SY Lim, B Stelea, X Han, T Pasquier (2021). Secure Namespaced Kernel Audit for Containers. ACM SoCC'21.

X Han, X Yu, T Pasquier, D Li, J Rhee, J Mickens, M Seltzer, C Haifeng (2021). SIGL: Securing Software Installations Through Deep Graph Learning. USENIX Sec'21.

X Han, T Pasquier, A Bates, J Mickens, M Seltzer (2020). UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats. NDSS'20.

T Pasquier, X Han, T Moyer, A Bates, O Hermant, D Eyers, J Bacon, M Seltzer (2018). Runtime Analysis of Whole-System Provenance. In CCS ‘18.

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T Pasquier, X Han, M Goldstein, T Moyer, D Eyers, M Seltzer, J Bacon (2017). Practical Whole-System Provenance Capture. In SoCC ‘17.

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