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Improving Privacy and Utility in Aggregate Data: A Hybrid Approach
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  • Samuel Nartey Kofie,
  • Ivy Min-Zhang,
  • Kai Chen,
  • Wei Percy
Samuel Nartey Kofie
The University of Waikato

Corresponding Author:[email protected]

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Ivy Min-Zhang
Nantong University
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Kai Chen
Nantong University
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Wei Percy
Changchun University of Technology
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Abstract

The increasing need to protect individual privacy in data releases has led to significant advancements in privacy-preserving technologies. Differential Privacy (DP) offers robust privacy guarantees but often at the expense of data utility. On the other hand, data pooling, while improving utility, lacks formal privacy assurances. Our study introduces a novel hybrid method, termed PoolDiv, which combines differential privacy with data pooling to enhance both privacy guarantees and data utility. Through extensive simulations and real data analysis, we assess the performance of synthetic datasets generated via traditional DP methods, data pooling, and our proposed PoolDiv method, demonstrating the advantages of our hybrid approach in maintaining data utility while ensuring privacy.
16 Jul 2024Submitted to Security and Privacy
16 Jul 2024Submission Checks Completed
16 Jul 2024Assigned to Editor
16 Jul 2024Review(s) Completed, Editorial Evaluation Pending
18 Jul 2024Reviewer(s) Assigned
14 Oct 2024Editorial Decision: Revise Major