Byzantine-Resilient Federated Computation of Differentially Private Summary Statistics
Giulio Segalini (University of Neuchâtel, TU Delft - Electrical Engineering, Mathematics and Computer Science)
Maria Fernandes (Universidade de Lisboa, University of Copenhagen)
Jérémie Decouchant (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
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.
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