| SecureNN: 3-Party Secure Computation for Neural Network Training S Wagh, D Gupta, N Chandran Proceedings on Privacy Enhancing Technologies 2019 (3), 26-49, 2019 | 618* | 2019 |
| 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 | 310 | 2021 |
| Maliciously Secure Matrix Multiplication with Applications to Private Deep Learning H Chen, M Kim, I Razenshteyn, D Rotaru, Y Song, S Wagh International Conference on the Theory and Application of Cryptology and …, 2020 | 77 | 2020 |
| Elsa: Secure aggregation for federated learning with malicious actors M Rathee, C Shen, S Wagh, RA Popa 2023 IEEE Symposium on Security and Privacy (SP), 1961-1979, 2023 | 76 | 2023 |
| Towards probabilistic verification of machine unlearning DM Sommer, L Song, S Wagh, P Mittal arXiv preprint arXiv:2003.04247, 2020 | 75 | 2020 |
| Piranha: A {GPU} platform for secure computation JL Watson, S Wagh, RA Popa 31st USENIX Security Symposium (USENIX Security 22), 827-844, 2022 | 71 | 2022 |
| Rabbit: Efficient Comparison for Secure Multi-Party Computation E Makri, D Rotaru, F Vercauteren, S Wagh International Conference on Financial Cryptography and Data Security, 249-270, 2021 | 69 | 2021 |
| Differentially Private Oblivious RAM S Wagh, P Cuff, P Mittal Proceedings on Privacy Enhancing Technologies 2018 (4), 64-84, 2018 | 69* | 2018 |
| DP-Cryptography: Marrying differential privacy and cryptography in emerging applications S Wagh, X He, A Machanavajjhala, P Mittal Communications of the ACM (CACM), 2021 | 55 | 2021 |
| Camouflage: Memory traffic shaping to mitigate timing attacks Y Zhou, S Wagh, P Mittal, D Wentzlaff 2017 IEEE International Symposium on High Performance Computer Architecture …, 2017 | 49 | 2017 |
| Athena: Probabilistic verification of machine unlearning DM Sommer, L Song, S Wagh, P Mittal Proceedings on Privacy Enhancing Technologies, 2022 | 32 | 2022 |
| Guard placement attacks on path selection algorithms for Tor G Wan, A Johnson, R Wails, S Wagh, P Mittal Proceedings on Privacy Enhancing Technologies 2019 (4), 2019 | 32 | 2019 |
| Pika: Secure computation using function secret sharing over rings S Wagh Proceedings on Privacy Enhancing Technologies 2022 (4), 351-377, 2022 | 31 | 2022 |
| DPSelect: a differential privacy based guard relay selection algorithm for Tor H Hanley, Y Sun, S Wagh, P Mittal Proceedings on Privacy Enhancing Technologies 2019 (2), 2019 | 23 | 2019 |
| Private deep neural network training S Wagh, N Chandran, D Gupta US Patent App. 15/917,091, 2019 | 22* | 2019 |
| The pyramid scheme: Oblivious RAM for trusted processors M Costa, L Esswood, O Ohrimenko, F Schuster, S Wagh arXiv preprint arXiv:1712.07882, 2017 | 19 | 2017 |
| A General Framework of Homomorphic Encryption for Multiple Parties with Non-interactive Key-Aggregation H Kwak, D Lee, Y Song, S Wagh International Conference on Applied Cryptography and Network Security, 403-430, 2024 | 16* | 2024 |
| New Directions in Efficient Privacy-Preserving Machine Learning S Wagh Princeton University, 2020 | 7 | 2020 |
| Tunable oblivious RAM S Wagh, P Cuff, P Mittal US Patent 10,229,068, 2019 | 7 | 2019 |
| BarnOwl: Secure Comparisons using Silent Pseudorandom Correlation Generators S Wagh Cryptology ePrint Archive, 2022 | 4 | 2022 |
Prateek MittalProfessor, Princeton University在 princeton.edu 的电子邮件经过验证
