One-sided Personalized Differential Privacy Random Response Algorithm Driven by User Sensitive Weights

LIU Zhenhua; WANG Wenxin; DONG Xinfeng; WANG Baocang

doi:10.11999/JEIT250099

Volume 47 Issue 8

Aug. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2768-2779

LIU Zhenhua, WANG Wenxin, DONG Xinfeng, WANG Baocang. One-sided Personalized Differential Privacy Random Response Algorithm Driven by User Sensitive Weights[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2768-2779. doi: 10.11999/JEIT250099

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LIU Zhenhua, WANG Wenxin, DONG Xinfeng, WANG Baocang. One-sided Personalized Differential Privacy Random Response Algorithm Driven by User Sensitive Weights[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2768-2779. doi: 10.11999/JEIT250099

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One-sided Personalized Differential Privacy Random Response Algorithm Driven by User Sensitive Weights

doi: 10.11999/JEIT250099 cstr: 32379.14.JEIT250099

1.
School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
2.
Science and Technology on Communication Security Laboratory, Chengdu 610041, China
3.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China

Funds: The National Cryptologic Science Fund of China (2025NCSF02032), The Natural Science Foundation of Shaanxi Province (2022JZ-38), The National Natural Science Foundation of China (61807026), The Secure Communication Key Laboratory Stabilization Program Support Project (2023)

Received Date: 2025-02-20
Rev Recd Date: 2025-04-14

Available Online: 2025-04-15

Publish Date: 2025-08-27

Abstract

Abstract

Objective One-sided differential privacy has received increasing attention in privacy protection due to its ability to shield sensitive information. This mechanism ensures that adversaries cannot substantially reduce uncertainty regarding record sensitivity, thereby enhancing privacy. However, its use in practical datasets remains constrained. Specifically, the random response algorithm under one-sided differential privacy performs effectively only when the proportion of sensitive records is low, but yields limited results in datasets with high sensitivity ratios. Examples include medical records, financial transactions, and personal data in social networks, where sensitivity levels are inherently high. Existing algorithms often fail to meet privacy protection requirements in such contexts. This study proposes an extension of the one-sided differential privacy random response algorithm by introducing user-sensitive weights. The method enables efficient processing of highly sensitive datasets while substantially improving data utility and maintaining privacy guarantees, supporting secure analysis and application of high-sensitivity data. Methods This study proposes a one-sided personalized differential privacy random response algorithm comprising three key stages: sensitivity specification, personalized sampling, and fixed-value noise addition. In the sensitivity specification stage, user data are mapped to sensitivity weight values using a predefined sensitivity function. This function reflects both the relative importance of each record to the user and its quantified sensitivity level. The resulting sensitivity weights are then normalized to compute a comprehensive sensitivity weight for each user. In the personalized sampling stage, the data sampling probability is adjusted dynamically according to the user’s comprehensive sensitivity weight. Unlike uniform-probability sampling employed in conventional methods, this personalized approach reduces sampling bias and improves data representativeness, thereby enhancing utility. In the fixed-value noise addition stage, the noise amount is determined in proportion to the comprehensive sensitivity weight. In high-sensitivity scenarios, a larger noise value is added to reinforce privacy protection; in low-sensitivity scenarios, the noise is reduced to preserve data availability. This adaptive mechanism allows the algorithm to balance privacy protection with utility across different application contexts. Results and Discussions The primary innovations of this study are reflected in three areas. First, a one-sided personalized differential privacy random response algorithm is proposed, incorporating a sensitivity specification function to allocate personalized sensitivity weights to user data. This design captures user-specific sensitivity requirements across data attributes and improves system efficiency by minimizing user interaction. Second, a personalized sampling method based on comprehensive sensitivity weights is developed to support fine-grained privacy protection. Compared with conventional approaches, this method dynamically adjusts sampling strategies in response to user-specific privacy preferences, thereby increasing data representativeness while maintaining privacy. Third, the algorithm’s sensitivity shielding property is established through theoretical analysis, and its effectiveness is validated via simulation experiments. The results show that the proposed algorithm outperforms the traditional one-sided differential privacy random response algorithm in both data utility and robustness. In high-sensitivity scenarios, improvements in query accuracy and robustness are particularly evident. When the data follow a Laplace distribution, for the sum function, the Root Mean Square Error (RMSE) produced by the proposed algorithm is approximately 76.67% of that generated by the traditional algorithm, with the threshold upper bound set to 0.6 (Fig. 4(c)). When the data follow a normal distribution, in the coefficient of variation function, the RMSE produced by the proposed algorithm remains below 200 regardless of whether the upper bound of the threshold t is 0.7, 0.8, or 0.9, while the RMSE of the traditional algorithm consistently exceeds 200 (Fig. 5(g,h,i)). On real-world datasets, the proposed algorithm achieves higher data utility across all three evaluated functions compared with the traditional approach (Fig. 6). Conclusions The proposed one-sided personalized differential privacy random response algorithm achieves effective performance under an equivalent level of privacy protection. It is applicable not only in datasets with a low proportion of sensitive records but also in those with high sensitivity, such as healthcare and financial transaction data. By integrating sensitivity specification, personalized sampling, and fixed-value noise addition, the algorithm balances privacy protection with data utility in complex scenarios. This approach offers reliable technical support for the secure analysis and application of highly sensitive data. Future work may investigate the extension of this algorithm to scenarios involving correlated data in relational databases.
- Privacy protection,
- One-sided differential privacy,
- Sensitivity masking,
- Sensitivity specification,
- Personalized sampling

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References(34)

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