Srinivas Devadas

Massachusetts Institute of Technology — United States

Computer Science · Physical Sciences

90h-index33.8kcitations545works3.42yr avg
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Research Topics

VLSI and Analog Circuit Testing(121), Parallel Computing and Optimization Techniques(111), Embedded Systems Design Techniques(92), Low-power high-performance VLSI design(83), Physical Unclonable Functions (PUFs) and Hardware Security(76)

Publications545 total

PAC-Private Responses with Adversarial Composition
ArXiv.org·2026Open Access
PAC-Private Responses with Adversarial Composition
arXiv (Cornell University)·2026Open Access
Making Sense of Private Advertising: A Principled Approach to a Complex Ecosystem
Proceedings on Privacy Enhancing Technologies·2026Open Access
Pseudorandom Correlation Functions for Garbled Circuits
Lecture notes in computer science·2025Open Access
One-Sided Bounded Noise: Theory, Optimization Algorithms and Applications
2025Open Access
Remote Direct Code Execution
2025Open Access
Expected Constant Round Byzantine Broadcast under Dishonest Majority
Journal of the ACM·2025· 1 cited
PAC-Private Algorithms
2025· 2 cited
Toward a Universal Cryptographic Accelerator
Computer·2025
QuietOT: Lightweight Oblivious Transfer with a Public-Key Setup
Lecture notes in computer science·2024· 8 cited
Formal Privacy Proof of Data Encoding: The Possibility and Impossibility of Learnable Encryption
2024· 2 citedOpen Access
Accelerating Zero-Knowledge Proofs Through Hardware-Algorithm Co-Design
2024· 13 cited
A Roadmap for End-to-End Privacy and Security in Generative AI
2024· 3 citedOpen Access
A Tensor Compiler with Automatic Data Packing for Simple and Efficient Fully Homomorphic Encryption
Proceedings of the ACM on Programming Languages·2024· 18 citedOpen Access
Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential Privacy with Optimal Gaussian Noise and Application to Deep Learning
2023· 9 citedOpen Access
Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential Privacy with Optimal Gaussian Noise and Application to Deep Learning
arXiv (Cornell University)·2023· 1 citedOpen Access
Citadel: Simple Spectre-Safe Isolation For Real-World Programs That Share Memory
arXiv (Cornell University)·2023Open Access
A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction
2023· 14 cited
Trellis: Robust and Scalable Metadata-private Anonymous Broadcast
2023· 8 cited
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
Lecture notes in computer science·2023· 5 cited
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Frequent Co-authors

G. Edward Suh(15), Marten van Dijk(14), Blaise Gassend(9), Dwaine Clarke(8), Abhijit Ghosh(5), Farinaz Koushanfar(4), Christopher W. Fletcher(4), Xiangyao Yu(4), Kurt Keutzer(4), Meng-Day Yu(3), Daihyun Lim(3), Elaine Shi(3), Ling Ren(3), Jacob White(3), A. Richard Newton(3), Mehrdad Majzoobi(3), Ulrich Rührmair(2), Frank Sehnke(2), Jan Sölter(2), Gideon Dror(2)