Private Analytics via Streaming, Sketching, and Silently Verifiable Proofs
Mayank Rathee, Yuwen Zhang, Henry Corrigan-Gibbs, Raluca Ada Popa
IEEE Symposium on Security and Privacy 2024 · Day 2 · Continental Ballroom 6
This talk introduces Whisper, a novel system designed to significantly enhance the efficiency of private analytics, particularly for scenarios involving a large number of users and sensitive data. The core problem Whisper addresses is how to compute aggregate statistics (like histograms or heavy hitters) over user data without compromising individual user privacy, even if some servers are compromised by an adversary. While existing solutions leverage **secret sharing** and **zero-knowledge proofs (ZKPs)** to achieve privacy and correctness, they suffer from substantial communication and storage overheads that scale linearly with the number of clients, making them impractical for large-scale deployments.
AI review
Whisper is a groundbreaking paper that finally cracks the scalability problem for private analytics. The introduction of Silently Verifiable Proofs (SVPs) is a genuinely novel cryptographic primitive, enabling batched ZKP verification that slashes server-to-server communication by 50x. This work directly addresses a critical bottleneck, making large-scale privacy-preserving data aggregation economically viable.