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Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism

Summary: XOR-based DP for binary- and matrix-valued queries with matrix-Bernoulli noise; privacy and utility guaranteed. Heuristic learning minimizes squared error under epsilon-DP; uses exact HMC sampling; shows DP utility gains in classification and networks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12599
Venue
VLDB
Year
2021
Pagerank
5.9468785e-05
Overall Rank
4,754 | 66.93%
DOI
10.14778/3446095.3446106

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
642 Private Analysis of Graph Structure 2011 VLDB 0.00018755196
2,226 Publishing Graph Degree Distribution with Node Differential Privacy 2016 SIGMOD 9.2421776e-05
2,227 Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies 2014 SIGMOD 9.2421238e-05
3,172 Bayesian Differential Privacy on Correlated Data 2015 SIGMOD 7.4411955e-05
4,461 Pufferfish Privacy Mechanisms for Correlated Data 2017 SIGMOD 6.1616828e-05
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