Paper 2020/1561

Cryptonite: A Framework for Flexible Time-Series Secure Aggregation with Online Fault Tolerance

Ryan Karl and Jonathan Takeshita and Taeho Jung

Abstract

Private stream aggregation (PSA) allows an untrusted data aggregator to compute statistics over a set of multiple participants' data while ensuring the data remains private. Existing works rely on a trusted party to enable an aggregator to achieve offline fault tolerance, but in the real world this may not be practical. We develop a new framework that supports PSA in a way that is robust to online user faults, while still supporting a strong guarantee on each individual’s privacy. We first must define a new level of security in the presence of online faults and malicious adversaries because the existing definition does not account for online faults. After this we describe a general framework that allows existing work to reach this new level of security. Furthermore, we develop the first protocol that provably reaches this level of security by leveraging trusted hardware. After we develop a methodology to outsource computationally intensive work to higher performance devices, while still allowing for strong privacy, we reach new levels of scalability and communication efficiency over existing work seeking to support offline fault tolerance, and achieve differential privacy.