[Resource Topic] 2022/590: Secure Merge in Linear Time and O(log log N) Rounds

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Secure Merge in Linear Time and O(log log N) Rounds

Authors: Mark Blunk, Paul Bunn, Samuel Dittmer, Steve Lu, Rafail Ostrovsky


Secure merge considers the problem of combining two sorted lists (which are either held separately by two parties, or held by two parties in some privacy-preserving manner, e.g. via secret-sharing), and outputting a single merged (sorted) list in a privacy-preserving manner (typically the final list is encrypted or secret-shared amongst the original two parties). Just as algorithms for \textit{insecure} merge are faster than comparison-based sorting (\Theta(n) versus \Theta(n \log n) for lists of size n), we explore protocols for performing a \textit{secure} merge that are more performant than simply invoking a secure sort protocol. Namely, we construct a semi-honest protocol that requires O(n) communication and computation and O(\log \log n) rounds of communication. This matches the metrics of the insecure merge for communication and computation, although it does not match the O(1) round-complexity of insecure merge. Our protocol relies only on black-box use of basic secure primitives, like secure comparison and shuffle. Our protocol improves on previous work of [FNO22], which gave a O(n) communication and O(n) round complexity protocol, and other ``naive’’ approaches, such as the shuffle-sort paradigm, which has O(n \log n) communication and O(\log n) round complexity. It is also more efficient for most practical applications than either a garbled circuit or fully-homomorphic encryption (FHE) approach, which each require O(n \log n) communication or computation and have O(1) round complexity. There are several applications that stand to benefit from our result, including secure sort (in cases where two or more parties have access to their own list of data, secure sort reduces to secure merge since the parties can first sort their own data locally), which in-turn has implications for more efficient private set intersection (PSI) protocols; as well as secure mutable database storage and search, whereby secure merge can be used to insert new rows into an existing database. In building our secure merge protocol, we develop several subprotocols that may be of independent interest. For example, we develop a protocol for secure asymmetric merge (where one list is much larger than the other), which matches theoretic lower-bounds for all three metrics (assuming the ratio of list sizes is small enough).

ePrint: https://eprint.iacr.org/2022/590

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