Karn Seth
I'm a Software Engineer in the Privacy and Data Protection Office (PDPO), where I work mainly on various flavors of Private Join and Compute, and am a primary maintainer of the associated open source repository. More broadly, I'm very interested in the use of Secure Multiparty Computation for privacy preserving measurement, analytics and machine learning, with a special focus on practical deployment.
In the past, I've worked with folks on Federated Machine Learning and Analytics, including the development of the Secure Aggregation protocol, which was featured in the Federated Learning Comic.
I've had the pleasure of hosting/co-hosting an incredible group of summer interns:
- 2021,2022: Stan Peceny (Georgia Tech), Amit Agarwal (University of Illinois, Urbana Champaign)
- 2020: Mahimna Kelkar (Cornell Tech), Le Phi Hung (Now at Google)
- 2019: Ni Trieu (Now at Arizona State University), Marshall Ball (Now at NYU)
- 2018: Peihan Miao (Now at Brown University)
- 2017: Jonathan Frankle (MIT)
- 2016: Antonio Marcedone (Now at Zoom)
Research Areas
Authored Publications
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Communication–Computation Trade-offs in PIR
Asra Ali
Tancrède Lepoint
Sarvar Patel
Phillipp Schoppmann
Kevin Yeo
30th USENIX Security Symposium (2021)
Two-Sided Malicious Security for Private Intersection-Sum with Cardinality
Peihan Miao
Sarvar Patel
Advances in Cryptology – CRYPTO 2020 (2020), pp. 3-33
Private Intersection-Sum Protocols with Applications to Attributing Aggregate Ad Conversions
Mihaela Ion
Benjamin Kreuter
Erhan Nergiz
Sarvar Patel
Shobhit Saxena
David Shanahan
2020 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 370-389
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Antonio Marcedone
Benjamin Kreuter
Sarvar Patel
Vladimir Ivanov
CCS (2017)
Practical Secure Aggregation for Federated Learning on User-Held Data
Vladimir Ivanov
Ben Kreuter
Antonio Marcedone
Sarvar Patel
NIPS Workshop on Private Multi-Party Machine Learning (2016)
