Differentially Private One Permutation Hashing
Keywords: hash, data compression, privacy
Abstract: Minwise hashing (MinHash) is a standard hashing algorithm for large-scale search and learning with the binary Jaccard similarity. One permutation hashing (OPH) is an effective and efficient alternative of MinHash which splits the data into K bins and generates hash values within each bin. In this paper, to protect the privacy of the output sketches, we combine differential privacy (DP) with OPH, and propose DP-OPH framework with three variants: DP-OPH-fix, DP-OPH-re and DP-OPH-rand, depending on the densification strategy to deal with empty bins in OPH. Detailed algorithm design and privacy and utility analysis are provided. The proposed DP-OPH methods significantly improves the DP minwise hashing (DP-MH) alternative in the literature. Experiments on similarity search confirm the effectiveness of our proposed algorithms. We also provide an extension to real-value data, named DP-BCWS, in the appendix.
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Submission Number: 13876
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