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Adaptive utility optimization for personalized local differential privacy.

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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]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 187995323
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Adaptive utility optimization for personalized local differential privacy.
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  Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Linhai%22">Cheng, Linhai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lan%2C+Qiujun%22">Lan, Qiujun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lanqiujun@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yin%2C+Xinyi%22">Yin, Xinyi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Shiyu%22">Jia, Shiyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Lin%22">Sun, Lin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+proofs%22">Mathematical proofs</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187995323
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      – Type: doi
        Value: 10.1016/j.eswa.2025.128750
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Privacy
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Mathematical proofs
        Type: general
      – SubjectFull: Estimation theory
        Type: general
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      – TitleFull: Adaptive utility optimization for personalized local differential privacy.
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            NameFull: Cheng, Linhai
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            NameFull: Sun, Lin
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            – D: 15
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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              Value: 294
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