Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference
With the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ privacy disclosure. Current research ignores the impact of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Cryptography |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2410-387X/7/1/11 |
| _version_ | 1797612549703729152 |
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| author | Yihong Guo Jinxin Zuo Ziyu Guo Jiahao Qi Yueming Lu |
| author_facet | Yihong Guo Jinxin Zuo Ziyu Guo Jiahao Qi Yueming Lu |
| author_sort | Yihong Guo |
| collection | DOAJ |
| description | With the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ privacy disclosure. Current research ignores the impact of disclosing user non-sensitive attributes under a single scenario of data sharing and lacks consideration of users’ privacy preferences. This paper constructs a data-sharing privacy metrics model based on information entropy and group privacy preferences. Use information theory to model the correlation of the privacy metrics problem, the improved entropy weight algorithm to measure the overall privacy of the data, and the analytic hierarchy process to correct user privacy preferences. Experiments show that this privacy metrics model can better quantify data privacy than conventional methods, provide a reliable evaluation mechanism for privacy security in data sharing and publishing scenarios, and help to enhance data privacy protection. |
| first_indexed | 2024-03-11T06:42:47Z |
| format | Article |
| id | doaj.art-6be80679c75a40c2a3b190250beaa8c9 |
| institution | Directory Open Access Journal |
| issn | 2410-387X |
| language | English |
| last_indexed | 2024-03-11T06:42:47Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cryptography |
| spelling | doaj.art-6be80679c75a40c2a3b190250beaa8c92023-11-17T10:27:43ZengMDPI AGCryptography2410-387X2023-03-01711110.3390/cryptography7010011Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy PreferenceYihong Guo0Jinxin Zuo1Ziyu Guo2Jiahao Qi3Yueming Lu4School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWith the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ privacy disclosure. Current research ignores the impact of disclosing user non-sensitive attributes under a single scenario of data sharing and lacks consideration of users’ privacy preferences. This paper constructs a data-sharing privacy metrics model based on information entropy and group privacy preferences. Use information theory to model the correlation of the privacy metrics problem, the improved entropy weight algorithm to measure the overall privacy of the data, and the analytic hierarchy process to correct user privacy preferences. Experiments show that this privacy metrics model can better quantify data privacy than conventional methods, provide a reliable evaluation mechanism for privacy security in data sharing and publishing scenarios, and help to enhance data privacy protection.https://www.mdpi.com/2410-387X/7/1/11privacy metricsdata securityinformation entropyprivacy preference |
| spellingShingle | Yihong Guo Jinxin Zuo Ziyu Guo Jiahao Qi Yueming Lu Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference Cryptography privacy metrics data security information entropy privacy preference |
| title | Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference |
| title_full | Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference |
| title_fullStr | Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference |
| title_full_unstemmed | Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference |
| title_short | Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference |
| title_sort | data sharing privacy metrics model based on information entropy and group privacy preference |
| topic | privacy metrics data security information entropy privacy preference |
| url | https://www.mdpi.com/2410-387X/7/1/11 |
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