Byzantine-Resilient Federated Computation of Differentially Private Summary Statistics

Summary statistics are essential to analyse large datasets in various fields, including financial and medical research. Federated computations enhance statistical power by combining geo-distributed datasets while ensuring compliance with data protection regulations, privacy guarantees, and resilience against intrusions. We present Tides, a federated framework leveraging Trusted Execution Environments (TEEs) to defend against adversaries controlling up to f of the N datacenters. We present an instantiation of Tides using genomic (GWAS) statistics. We address TEE-specific attack vectors, including communication blocking and side-channel attacks. Tides follows the following three key steps: (1) TEEs share statistical results through reliable broadcast and run a randomized crash-tolerant binary consensus algorithm to identify the datasets that are available; (2) TEEs enforce differential privacy with ad hoc noise; and (3) TEEs run memory-oblivious algorithms to compute the final summary statistics. We implemented Tides with Intel SGX enclaves and demonstrated its practicality with three datasets.