Adaptive personalized privacy-preserving data collection scheme with local differential privacy
Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In most recent works, it is assumed that the privacy parameter is determined solely by collectors and th...
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| Format: | Article |
| Language: | English |
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Springer
2024-04-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157824001319 |
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| author | Haina Song Hua Shen Nan Zhao Zhangqing He Wei Xiong Minghu Wu Mingwu Zhang |
| author_facet | Haina Song Hua Shen Nan Zhao Zhangqing He Wei Xiong Minghu Wu Mingwu Zhang |
| author_sort | Haina Song |
| collection | DOAJ |
| description | Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In most recent works, it is assumed that the privacy parameter is determined solely by collectors and then dispatched to all participants. However, it is inelegant and unpromising for all participants to accept the same level of privacy preservation due to their personalized preferences. Here, an adaptive data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods are adaptively chosen by data participants according to their personalized privacy preferences. The adaptive boundary based on the minimum mean square error (MSE) is theoretically and accurately derived to allow participants to adaptively choose the best perturbation method. Then, a weighted combination method is demonstrated to do effective data aggregation from multiple privacy groups. Moreover, an expanded data strategy (EDS) with multiple privacy perturbations is presented to equivalently increase the sample size without harming others privacy, thereby further improving the accuracy of statistics. Finally, the experiments show that the proposed scheme performs better than the previous proposal in terms of MSE and average error rate (AER), especially using the EDS method. |
| format | Article |
| id | doaj-art-2d5e6e7880d84bb6838ec32454e7f429 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-2d5e6e7880d84bb6838ec32454e7f4292025-08-20T03:55:02ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782024-04-0136410204210.1016/j.jksuci.2024.102042Adaptive personalized privacy-preserving data collection scheme with local differential privacyHaina Song0Hua Shen1Nan Zhao2Zhangqing He3Wei Xiong4Minghu Wu5Mingwu Zhang6Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan, 430068, PR China; Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, PR ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, 430068, PR ChinaHubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan, 430068, PR China; Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, PR ChinaHubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan, 430068, PR China; Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, PR ChinaHubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan, 430068, PR China; Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, PR ChinaHubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan, 430068, PR China; Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, PR China; Corresponding author.School of Computer Science, Hubei University of Technology, Wuhan, 430068, PR ChinaLocal differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In most recent works, it is assumed that the privacy parameter is determined solely by collectors and then dispatched to all participants. However, it is inelegant and unpromising for all participants to accept the same level of privacy preservation due to their personalized preferences. Here, an adaptive data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods are adaptively chosen by data participants according to their personalized privacy preferences. The adaptive boundary based on the minimum mean square error (MSE) is theoretically and accurately derived to allow participants to adaptively choose the best perturbation method. Then, a weighted combination method is demonstrated to do effective data aggregation from multiple privacy groups. Moreover, an expanded data strategy (EDS) with multiple privacy perturbations is presented to equivalently increase the sample size without harming others privacy, thereby further improving the accuracy of statistics. Finally, the experiments show that the proposed scheme performs better than the previous proposal in terms of MSE and average error rate (AER), especially using the EDS method.http://www.sciencedirect.com/science/article/pii/S1319157824001319Adaptive personalized privacy-preservingLocal differential privacyMinimum mean square errorExpanded data strategy |
| spellingShingle | Haina Song Hua Shen Nan Zhao Zhangqing He Wei Xiong Minghu Wu Mingwu Zhang Adaptive personalized privacy-preserving data collection scheme with local differential privacy Journal of King Saud University: Computer and Information Sciences Adaptive personalized privacy-preserving Local differential privacy Minimum mean square error Expanded data strategy |
| title | Adaptive personalized privacy-preserving data collection scheme with local differential privacy |
| title_full | Adaptive personalized privacy-preserving data collection scheme with local differential privacy |
| title_fullStr | Adaptive personalized privacy-preserving data collection scheme with local differential privacy |
| title_full_unstemmed | Adaptive personalized privacy-preserving data collection scheme with local differential privacy |
| title_short | Adaptive personalized privacy-preserving data collection scheme with local differential privacy |
| title_sort | adaptive personalized privacy preserving data collection scheme with local differential privacy |
| topic | Adaptive personalized privacy-preserving Local differential privacy Minimum mean square error Expanded data strategy |
| url | http://www.sciencedirect.com/science/article/pii/S1319157824001319 |
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