| A uror: defending against poisoning attacks in collaborative deep learning systems S Shen, S Tople, P Saxena Proceedings of the 32nd Annual Conference on Computer Security Applications …, 2016 | 413 | 2016 |
| Domain generalization using causal matching D Mahajan, S Tople, A Sharma International Conference on Machine Learning, 7313-7324, 2021 | 338 | 2021 |
| Panoply: Low-TCB Linux Applications With SGX Enclaves. S Shinde, D Le Tien, S Tople, P Saxena NDSS, 2017 | 322 | 2017 |
| Falcon: Honest-majority maliciously secure framework for private deep learning S Wagh, S Tople, F Benhamouda, E Kushilevitz, P Mittal, T Rabin Proceedings on Privacy Enhancing Technologies 2021 (1), 188-208, 2021 | 309 | 2021 |
| A traceability analysis of monero’s blockchain A Kumar, C Fischer, S Tople, P Saxena European Symposium on Research in Computer Security, 153-173, 2017 | 251 | 2017 |
| Analyzing leakage of personally identifiable information in language models N Lukas, A Salem, R Sim, S Tople, L Wutschitz, S Zanella-Béguelin arXiv preprint arXiv:2302.00539, 2023 | 193 | 2023 |
| Analyzing information leakage of updates to natural language models S Zanella-Béguelin, L Wutschitz, S Tople, V Rühle, A Paverd, ... Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications …, 2020 | 131 | 2020 |
| Privado: Practical and secure DNN inference S Tople, K Grover, S Shinde, R Bhagwan, R Ramjee arXiv preprint arXiv:1810.00602, 2018 | 104* | 2018 |
| Leakage of Dataset Properties in Multi-Party Machine Learning W Zhang, S Tople, O Ohrimenko 30th {USENIX} Security Symposium ({USENIX} Security 21), 2687-2704, 2021 | 103* | 2021 |
| Bayesian estimation of differential privacy S Zanella-Béguelin, L Wutschitz, S Tople, A Salem, V Rühle, A Paverd, ... arXiv preprint arXiv:2206.05199, 2022 | 41 | 2022 |
| SoK: Let The Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning A Salem, G Cherubin, D Evans, B Köpf, A Paverd, A Suri, S Tople, ... arXiv preprint arXiv:2212.10986, 2022 | 37 | 2022 |
| Alleviating Privacy Attacks via Causal Learning S Tople, A Sharma, A Nori International Conference on Machine Learning, 9537-9547, 2020 | 35 | 2020 |
| Collaborative Machine Learning Markets with Data-Replication-Robust Payments O Ohrimenko, S Tople, S Tschiatschek arXiv preprint arXiv:1911.09052, 2019 | 34 | 2019 |
| AUTOCRYPT: enabling homomorphic computation on servers to protect sensitive web content S Tople, S Shinde, Z Chen, P Saxena Proceedings of the 2013 ACM SIGSAC conference on Computer & communications …, 2013 | 33 | 2013 |
| Grey-box Extraction of Natural Language Models S Zanella-Beguelin, S Tople, A Paverd, B Köpf International Conference on Machine Learning, 12278-12286, 2021 | 30 | 2021 |
| On the trade-offs in oblivious execution techniques S Tople, P Saxena International Conference on Detection of Intrusions and Malware, and …, 2017 | 30 | 2017 |
| Protecting sensitive web content from client-side vulnerabilities with CRYPTONS X Dong, Z Chen, H Siadati, S Tople, P Saxena, Z Liang Proceedings of the 2013 ACM SIGSAC conference on Computer & communications …, 2013 | 28 | 2013 |
| PRO-ORAM: Practical Read-Only Oblivious {RAM} S Tople, Y Jia, P Saxena 22nd International Symposium on Research in Attacks, Intrusions and Defenses …, 2019 | 27* | 2019 |
| Protecting machine learning models from privacy attacks A Sharma, AV Nori, SS TOPLE US Patent App. 16/559,444, 2021 | 21 | 2021 |
| Membership Inference Attacks and Generalization: A Causal Perspective T Baluta, S Shen, S Hitarth, S Tople, P Saxena arXiv preprint arXiv:2209.08615, 2022 | 20 | 2022 |
Prateek SaxenaNational University of SingaporeE-mailová adresa ověřena na: comp.nus.edu.sg
