Protecting sensitive web content from client-side vulnerabilities with CRYPTONS. - Xinshu Dong; Zhaofeng Chen; Hossein Siadati; Shruti Tople; Prateek Saxena; Zhenkai Liang (CCS, 2013)

AUTOCRYPT: enabling homomorphic computation on servers to protect sensitive web content. - Shruti Tople; Shweta Shinde; Zhaofeng Chen; Prateek Saxena (CCS, 2013)

Faster Secure Arithmetic Computation Using Switchable Homomorphic Encryption. - Hoon Wei Lim; Shruti Tople; Prateek Saxena; Ee-Chien Chang (IACR Cryptol. ePrint Arch., 2014)

Auror: defending against poisoning attacks in collaborative deep learning systems. - Shiqi Shen; Shruti Tople; Prateek Saxena (ACSAC, 2016)

OblivP2P: An Oblivious Peer-to-Peer Content Sharing System. - Yaoqi Jia; Tarik Moataz; Shruti Tople; Prateek Saxena (USENIX Security Symposium, 2016)

On the Trade-Offs in Oblivious Execution Techniques. - Shruti Tople; Prateek Saxena (DIMVA, 2017)

A Traceability Analysis of Monero's Blockchain. - Amrit Kumar 001; Clément Fischer; Shruti Tople; Prateek Saxena (ESORICS (2), 2017)

Panoply: Low-TCB Linux Applications With SGX Enclaves. - Shweta Shinde; Dat Le Tien; Shruti Tople; Prateek Saxena (NDSS, 2017)

Robust Synchronous P2P Primitives Using SGX Enclaves. - Yaoqi Jia; Shruti Tople; Tarik Moataz; Deli Gong; Prateek Saxena; Zhenkai Liang (IACR Cryptol. ePrint Arch., 2017)

PermuteRam: Optimizing Oblivious Computation for Efficiency. - Shruti Tople; Hung Dang; Prateek Saxena; Ee-Chien Chang (IACR Cryptol. ePrint Arch., 2017)

VeriCount: Verifiable Resource Accounting Using Hardware and Software Isolation. - Shruti Tople; Soyeon Park; Min Suk Kang; Prateek Saxena (ACNS, 2018)

Privado: Practical and Secure DNN Inference. - Shruti Tople; Karan Grover; Shweta Shinde; Ranjita Bhagwan; Ramachandran Ramjee (CoRR, 2018)

PRO-ORAM: Constant Latency Read-Only Oblivious RAM. - Shruti Tople; Yaoqi Jia; Prateek Saxena (IACR Cryptol. ePrint Arch., 2018)

PRO-ORAM: Practical Read-Only Oblivious RAM. - Shruti Tople; Yaoqi Jia; Prateek Saxena (RAID, 2019)

Collaborative Machine Learning Markets with Data-Replication-Robust Payments. - Olga Ohrimenko; Shruti Tople; Sebastian Tschiatschek (CoRR, 2019)

On the Intrinsic Privacy of Stochastic Gradient Descent. - Stephanie L. Hyland; Shruti Tople (CoRR, 2019)

Analyzing Privacy Loss in Updates of Natural Language Models. - Shruti Tople; Marc Brockschmidt; Boris Köpf; Olga Ohrimenko; Santiago Zanella-Béguelin (CoRR, 2019)

Analyzing Information Leakage of Updates to Natural Language Models. - Santiago Zanella-Béguelin; Lukas Wutschitz; Shruti Tople; Victor Rühle; Andrew Paverd; Olga Ohrimenko; Boris Köpf; Marc Brockschmidt (CCS, 2020)

Robust P2P Primitives Using SGX Enclaves. - Yaoqi Jia; Shruti Tople; Tarik Moataz; Deli Gong; Prateek Saxena; Zhenkai Liang (ICDCS, 2020)

Alleviating Privacy Attacks via Causal Learning. - Shruti Tople; Amit Sharma 007; Aditya Nori (ICML, 2020)

To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers. - Bijeeta Pal; Shruti Tople (CoRR, 2020)

Dataset-Level Attribute Leakage in Collaborative Learning. - Wanrong Zhang 001; Shruti Tople; Olga Ohrimenko (CoRR, 2020)

Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces. - Dongge Han; Shruti Tople; Alex Rogers; Michael J. Wooldridge; Olga Ohrimenko; Sebastian Tschiatschek (CoRR, 2020)

SOTERIA: In Search of Efficient Neural Networks for Private Inference. - Anshul Aggarwal; Trevor E. Carlson; Reza Shokri; Shruti Tople (CoRR, 2020)

Grey-box Extraction of Natural Language Models. - Santiago Zanella-Béguelin; Shruti Tople; Andrew Paverd; Boris Köpf (ICML, 2021)

Domain Generalization using Causal Matching. - Divyat Mahajan; Shruti Tople; Amit Sharma 007 (ICML, 2021)

Leakage of Dataset Properties in Multi-Party Machine Learning. - Wanrong Zhang 001; Shruti Tople; Olga Ohrimenko (USENIX Security Symposium, 2021)

Causally Constrained Data Synthesis for Private Data Release. - Varun Chandrasekaran; Darren Edge; Somesh Jha; Amit Sharma 007; Cheng Zhang 005; Shruti Tople (CoRR, 2021)

The Connection between Out-of-Distribution Generalization and Privacy of ML Models. - Divyat Mahajan; Shruti Tople; Amit Sharma 007 (CoRR, 2021)

Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning. - Sameer Wagh; Shruti Tople; Fabrice Benhamouda; Eyal Kushilevitz; Prateek Mittal; Tal Rabin (Proc. Priv. Enhancing Technol., 2021)

Membership Inference Attacks and Generalization: A Causal Perspective. - Teodora Baluta; Shiqi Shen; S. Hitarth; Shruti Tople; Prateek Saxena (CCS, 2022)

Invariant Aggregator for Defending Federated Backdoor Attacks. - Xiaoyang Wang; Dimitrios Dimitriadis; Sanmi Koyejo; Shruti Tople (CoRR, 2022)

Mace: A flexible framework for membership privacy estimation in generative models. - Yixi Xu; Sumit Mukherjee; Xiyang Liu; Shruti Tople; Rahul Dodhia; Juan M. Lavista Ferres (Trans. Mach. Learn. Res., 2022)

Bayesian Estimation of Differential Privacy. - Santiago Zanella-Béguelin; Lukas Wutschitz; Shruti Tople; Ahmed Salem 001; Victor Rühle; Andrew Paverd; Mohammad Naseri; Boris Köpf; Daniel Jones (ICML, 2023)

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage. - Valentin Hartmann; Léo Meynent; Maxime Peyrard; Dimitrios Dimitriadis; Shruti Tople; Robert West 001 (SaTML, 2023)

Analyzing Leakage of Personally Identifiable Information in Language Models. - Nils Lukas; Ahmed Salem 001; Robert Sim; Shruti Tople; Lukas Wutschitz; Santiago Zanella-Béguelin (SP, 2023)

SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning. - Ahmed Salem 001; Giovanni Cherubin; David Evans 001; Boris Köpf; Andrew Paverd; Anshuman Suri; Shruti Tople; Santiago Zanella-Béguelin (SP, 2023)

Re-aligning Shadow Models can Improve White-box Membership Inference Attacks. - Ana-Maria Cretu 002; Daniel Jones; Yves-Alexandre de Montjoye; Shruti Tople (CoRR, 2023)

Why Train More? Effective and Efficient Membership Inference via Memorization. - Jihye Choi; Shruti Tople; Varun Chandrasekaran; Somesh Jha (CoRR, 2023)

SoK: Memorization in General-Purpose Large Language Models. - Valentin Hartmann; Anshuman Suri; Vincent Bindschaedler; David Evans 001; Shruti Tople; Robert West 001 (CoRR, 2023)

Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective. - Lukas Wutschitz; Boris Köpf; Andrew Paverd; Saravan Rajmohan; Ahmed Salem 001; Shruti Tople; Santiago Zanella-Béguelin; Menglin Xia; Victor Rühle (CoRR, 2023)

On the Efficacy of Differentially Private Few-shot Image Classification. - Marlon Tobaben; Aliaksandra Shysheya; John Bronskill; Andrew Paverd; Shruti Tople; Santiago Zanella-Béguelin; Richard E. Turner; Antti Honkela (Trans. Mach. Learn. Res., 2023)

Invariant Aggregator for Defending against Federated Backdoor Attacks. - Xiaoyang Wang; Dimitrios Dimitriadis; Sanmi Koyejo; Shruti Tople (AISTATS, 2024)

Closed-Form Bounds for DP-SGD against Record-level Inference. - Giovanni Cherubin; Boris Köpf; Andrew Paverd; Shruti Tople; Lukas Wutschitz; Santiago Zanella-Béguelin (USENIX Security Symposium, 2024)

Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting. - Ana-Maria Cretu 002; Daniel Jones; Yves-Alexandre de Montjoye; Shruti Tople (Proc. Priv. Enhancing Technol., 2024)

Permissive Information-Flow Analysis for Large Language Models. - Shoaib Ahmed Siddiqui; Radhika Gaonkar; Boris Köpf; David Krueger 001; Andrew Paverd; Ahmed Salem 001; Shruti Tople; Lukas Wutschitz; Menglin Xia; Santiago Zanella-Béguelin (Trans. Mach. Learn. Res., 2025)

The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text. - Matthieu Meeus; Lukas Wutschitz; Santiago Zanella-Béguelin; Shruti Tople; Reza Shokri (CoRR, 2025)

Securing AI Agents with Information-Flow Control. - Manuel Costa; Boris Köpf; Aashish Kolluri; Andrew Paverd; Mark Russinovich; Ahmed Salem 001; Shruti Tople; Lukas Wutschitz; Santiago Zanella-Béguelin (CoRR, 2025)