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Adaptive utility optimization for personalized local differential privacy.

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Bibliographic Details
Title: Adaptive utility optimization for personalized local differential privacy.
Authors: Cheng, Linhai1 (AUTHOR), Lan, Qiujun1 (AUTHOR) lanqiujun@hnu.edu.cn, Yin, Xinyi2 (AUTHOR), Jia, Shiyu1 (AUTHOR), Sun, Lin1 (AUTHOR)
Source: Expert Systems with Applications. Dec2025, Vol. 294, pN.PAG-N.PAG. 1p.
Subjects: Privacy, Mathematical optimization, Mathematical proofs, Estimation theory
Abstract: Users typically have varying privacy requirements when submitting their personal data to data collectors. To address this need, researchers have expanded existing Local Differential Privacy (LDP) methods into a personalized format, allowing users to select their privacy budget according to their individual privacy demands. However, these schemes uniformly apply an LDP method for privacy protection, neglecting the fact that different LDP methods yield varying data utility under the same privacy budget. To enhance data utility within personalized privacy protection, this paper proposes an adaptive utility optimization framework for personalized LDP and applies it to frequency estimation. Firstly, we present a mechanism for the adaptive matching of LDPs with minimal errors for users. This is accomplished by comparing the theoretical errors of different LDPs under personalized privacy budgets. Subsequently, we integrate this approach with a weighted combination optimization method to propose a novel adaptive utility optimization technique for personalized LDP. We also provide a theoretical proof of the effectiveness of our method in optimizing both privacy and utility. Finally, our method has been experimentally validated, demonstrating its efficacy as a tool for utility optimization, outperforming existing methods. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Users typically have varying privacy requirements when submitting their personal data to data collectors. To address this need, researchers have expanded existing Local Differential Privacy (LDP) methods into a personalized format, allowing users to select their privacy budget according to their individual privacy demands. However, these schemes uniformly apply an LDP method for privacy protection, neglecting the fact that different LDP methods yield varying data utility under the same privacy budget. To enhance data utility within personalized privacy protection, this paper proposes an adaptive utility optimization framework for personalized LDP and applies it to frequency estimation. Firstly, we present a mechanism for the adaptive matching of LDPs with minimal errors for users. This is accomplished by comparing the theoretical errors of different LDPs under personalized privacy budgets. Subsequently, we integrate this approach with a weighted combination optimization method to propose a novel adaptive utility optimization technique for personalized LDP. We also provide a theoretical proof of the effectiveness of our method in optimizing both privacy and utility. Finally, our method has been experimentally validated, demonstrating its efficacy as a tool for utility optimization, outperforming existing methods. [ABSTRACT FROM AUTHOR]
ISSN:09574174
DOI:10.1016/j.eswa.2025.128750