Hermetic : Privacy-preserving distributed analytics without ( most ) side channels

Trusted execution environment (TEE), such as Intel SGX, is an emerging technology that enables privacy-preserving distributed analytics on an untrusted cloud platform. TEEs, however, suffer from side channels, such as timing, memory and instruction access patterns, and message sizes that weaken their privacy guarantees. Existing attempts to mitigate these channels in analytics systems are unsatisfactory because they do not attempt to address multiple critical side channels simultaneously and employ mitigations that are inefficient, unprincipled, or difficult to use. In this paper, we present Hermetic, a data query processing system that offers a principled approach to mitigating four of the most critical digital side channels simultaneously. We introduce an oblivious execution environment that supports fast, non-oblivious computations without leakage, as well as fast secure query operators using this primitive to process large data sets. We apply the differentially private (DP) padding mechanism on execution time and output sizes of query operators, which avoids prohibitive padding overheads as in most prior solutions, with provable privacy. To achieve efficient DP padding in complex query, we further introduce an privacy-aware query planner that can optimize a query plan under user’s privacy constraints. Our experimental evaluation of a Hermetic prototype shows that it is competitive with previous privacy-preserving systems, even though it provides stronger privacy guarantees.