Zero-Knowledge Cloud Analytics
Zeying Zhu, Clarence Lam, Alexander Frolov, Ian Miers, Zaoxing Liu
摘要
We present zk-Analytics, a distributed cloud analytics system that enables publicly verifiable analytics without revealing raw logs or relying on trusted hardware in analytics providers' infrastructure. Today's cloud analytics are largely self-assertive: providers collect telemetry, perform aggregation, and report results, leaving external parties unable to verify correctness without access to sensitive data or trusted execution environments. zk-Analytics addresses this gap by augmenting analytics pipelines with lightweight append-only log commitments and verifiable aggregation and query execution using zero-knowledge proofs. The system cleanly separates online log commitment from offline, distributed batch aggregation and query verification, enabling scalability while keeping online overhead low. We implement zk-Analytics using a zkVM-based execution environment and evaluate it on real-world and synthetic workloads, demonstrating that verifiable, privacy-preserving cloud analytics is feasible for real-world cloud workloads. zk-Analytics is open-sourced at https://github.com/Froot-NetSys/zk-Analytics.
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