DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization
DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization
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DOI：
10.1109/tkde.2023.3265605
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发表时间：
2022-08
影响因子：
8.9
通讯作者：
Honglu Jiang;Hao-Chun Yu;Xiuzhen Cheng;Jian Pei;Robert Pless;Jiguo Yu
中科院分区：
文献类型：
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作者：
Honglu Jiang;Hao-Chun Yu;Xiuzhen Cheng;Jian Pei;Robert Pless;Jiguo Yu
实际应用中出现大量高维异构数据，这些数据往往发布给第三方进行数据分析、推荐、定向广告和可靠预测。然而，发布这些数据可能会泄露个人敏感信息，导致隐私侵犯问题日益受到关注。近年来，保护隐私的数据发布受到了广泛关注。不幸的是，高维数据的差分隐私发布仍然是一个具有挑战性的问题。在本文中，我们提出了一种差分隐私高维数据发布机制（DP2-Pub），该机制分两个阶段运行：基于马尔可夫毯的属性聚类阶段和不变后随机化（PRAM）阶段。具体来说，将属性分割成多个具有高簇内内聚和低簇间耦合的低维簇有助于获得隐私预算的合理分配，而满足局部差分隐私的双扰动机制有利于不变的PRAM确保统计信息不丢失，从而显着保留数据效用。我们还将DP2-Pub机制扩展到具有满足本地差分隐私的半诚实服务器的场景。我们对四个真实世界的数据集进行了广泛的实验，实验结果表明我们的机制可以在满足差异隐私的同时显着提高已发布数据的数据效用。
A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy.