175 citations · 262 across the 24 of their papers we have counts for
9 papers · 1 filter
Undetectable Conversations Between AI Agents via Pseudorandom Noise-Resilient Key Exchange
Vinod Vaikuntanathan, Or Zamir
AI agents are increasingly deployed to interact with other agents on behalf of users and organizations. We ask whether two such agents, operated by different entities, can carry ou…
Improving Algorithmic Efficiency using Cryptography
Vinod Vaikuntanathan, Or Zamir
Cryptographic primitives have been used for various non-cryptographic objectives, such as eliminating or reducing randomness and interaction. We show how to use cryptography to imp…
A system capable of verifiably and privately screening global DNA synthesis
Carsten Baum, Jens Berlips, Walther Chen +28
Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recog…
FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption
Rashmi Agrawal, Leo de Castro, Guowei Yang +5
FHE offers protection to private data on third-party cloud servers by allowing computations on the data in encrypted form. However, to support general-purpose encrypted computation…
Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures
Aparna Gupte, Neekon Vafa, Vinod Vaikuntanathan
We show direct and conceptually simple reductions between the classical learning with errors (LWE) problem and its continuous analog, CLWE (Bruna, Regev, Song and Tang, STOC 2021).…
Does Fully Homomorphic Encryption Need Compute Acceleration?
Leo de Castro, Rashmi Agrawal, Rabia Yazicigil +4
Fully Homomorphic Encryption (FHE) allows arbitrarily complex computations on encrypted data without ever needing to decrypt it, thus enabling us to maintain data privacy on third-…
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
Adam Yala, Homa Esfahanizadeh, Rafael G. L. D' Oliveira +6
Balancing the needs of data privacy and predictive utility is a central challenge for machine learning in healthcare. In particular, privacy concerns have led to a dearth of public…
How to Subvert Backdoored Encryption: Security Against Adversaries that Decrypt All Ciphertexts
Thibaut Horel, Sunoo Park, Silas Richelson +1
We study secure and undetectable communication in a world where governments can read all encrypted communications of citizens. We consider a world where the only permitted communic…
Gazelle: A Low Latency Framework for Secure Neural Network Inference
Chiraag Juvekar, Vinod Vaikuntanathan, Anantha Chandrakasan
The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem…