Paper 2020/1561

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

Ryan Karl, Jonathan Takeshita, Nirajan Koirla, 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 third party to enable an aggregator to achieve fault tolerance, that requires interactive recovery, but in the real world this may not be practical or secure. We develop a new formal framework for PSA that accounts for user faults, and can support non-interactive recovery, while still supporting strong individual privacy guarantees. We first must define a new level of security in the presence of faults and malicious adversaries because the existing definitions do not account for faults and the security implications of the recovery. After this we develop the first protocol that provably reaches this level of security, i.e., individual inputs are private even after the aggregator's recovery, and reach new levels of scalability and communication efficiency over existing work seeking to support fault tolerance. The techniques we develop are general, and can be used to augment any PSA scheme to support non-interactive fault recovery.

Note: Camera-ready version of an accepted paper (IEEE DCOSS 2021).