PRAC: Round-Efficient 3-Party MPC for Dynamic Data Structures
Authors: Sajin Sasy (University of Waterloo), Adithya Vadapalli (IIT Kanpur), Ian Goldberg (University of Waterloo)
Volume: 2024
Issue: 3
Pages: 692–714
DOI: https://doi.org/10.56553/popets-2024-0100
Abstract: We present Private Random Access Computations (PRAC), a 3-party Secure Multi-Party Computation (MPC) framework to support random-access data structure algorithms for MPC with efficient communication in terms of rounds and bandwidth. PRAC extends the state-of-the-art DORAM Duoram with a new implementation, more flexibility in how the DORAM memory is shared, and support for Incremental and Wide DPFs. We then use these DPF extensions to achieve algorithmic improvements in three novel oblivious data structure protocols for MPC. PRAC exploits the observation that a secure protocol for an algorithm can gain efficiency if the protocol explicitly reveals information leaked by the algorithm inherently. We first present an optimized binary search protocol that reduces the bandwidth from O(lg² n) to O(lg n) for obliviously searching over n items. We then present an oblivious heap protocol with rounds reduced from O(lg n) to O(lg lg n) for insertions, and bandwidth reduced from O(lg² n) to O(lg n) for extractions. Finally, we also present the first oblivious AVL tree protocol for MPC where no party learns the data or the structure of the AVL tree, and can support arbitrary insertions and deletions with O(lg n) rounds and bandwidth. We experimentally evaluate our protocols with realistic network settings for a wide range of memory sizes to demonstrate their efficiency. For instance, we observe our binary search protocol provides >27× and >3× improvements in wall-clock time and bandwidth respectively over other approaches for a memory with 2^26 items; for the same setting our heap's extract-min protocol achieves >31× speedup in wall-clock time and >13× reduction in bandwidth.
Keywords: oblivious data structures, secure multi-party computation, oblivious RAMs, distributed privacy
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