Publishing set-valued data via differential privacy

Author:

Chen Rui1,Mohammed Noman1,Fung Benjamin C. M.1,Desai Bipin C.1,Xiong Li2

Affiliation:

1. Concordia University, Montreal, Canada

2. Emory University

Abstract

Set-valued data provides enormous opportunities for various data mining tasks. In this paper, we study the problem of publishing set-valued data for data mining tasks under the rigorous differential privacy model. All existing data publishing methods for set-valued data are based on partition-based privacy models, for example k -anonymity, which are vulnerable to privacy attacks based on background knowledge. In contrast, differential privacy provides strong privacy guarantees independent of an adversary's background knowledge and computational power. Existing data publishing approaches for differential privacy, however, are not adequate in terms of both utility and scalability in the context of set-valued data due to its high dimensionality. We demonstrate that set-valued data could be efficiently released under differential privacy with guaranteed utility with the help of context-free taxonomy trees. We propose a probabilistic top-down partitioning algorithm to generate a differentially private release, which scales linearly with the input data size. We also discuss the applicability of our idea to the context of relational data. We prove that our result is (∈, δ)-useful for the class of counting queries, the foundation of many data mining tasks. We show that our approach maintains high utility for counting queries and frequent itemset mining and scales to large datasets through extensive experiments on real-life set-valued datasets.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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1. Preserving data privacy in machine learning systems;Computers & Security;2024-02

2. PUTS: Privacy-Preserving and Utility-Enhancing Framework for Trajectory Synthesization;IEEE Transactions on Knowledge and Data Engineering;2024-01

3. ODTT: Optimized Dynamic Taxonomy Tree with Differential Privacy;2023 IEEE International Conference on Big Data (BigData);2023-12-15

4. Towards Benchmarking Privacy Risk for Differential Privacy: A Survey;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15

5. Hasse sensitivity level: A sensitivity-aware trajectory privacy-enhanced framework with Reinforcement Learning;Future Generation Computer Systems;2023-05

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