Risk-Aware Individual Trajectory Data Publishing With Differential Privacy
Large-scale spatiotemporal data mining has created valuable insights into managing key areas of society and the economy. It has encouraged data owners to release/publish trajectory datasets. However, the ill-informed publication of such valuable datasets may lead to serious privacy implications for...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9311790/ |
| _version_ | 1819180440950407168 |
|---|---|
| author | Jianzhe Zhao Jie Mei Stan Matwin Yukai Su Yuancheng Yang |
| author_facet | Jianzhe Zhao Jie Mei Stan Matwin Yukai Su Yuancheng Yang |
| author_sort | Jianzhe Zhao |
| collection | DOAJ |
| description | Large-scale spatiotemporal data mining has created valuable insights into managing key areas of society and the economy. It has encouraged data owners to release/publish trajectory datasets. However, the ill-informed publication of such valuable datasets may lead to serious privacy implications for individuals. Moreover, as a major goal of data protection, balancing privacy and utility remains a challenging problem due to the diversity of spatiotemporal data. However, the user dimension was not considered for traditional frameworks, which limits the application at the global level as opposed to the user level. Many researchers overcome this issue by assuming that a user in the dataset generates only one trajectory. Actually, a user always generates multiple and repetitive trajectories during observation. Only considering one trajectory for one user may cause insufficient privacy protection at the trajectory level alone, as a user's privacy can be manifested in many trajectories collectively. In addition, it demonstrates strong user correlation when using multiple and repetitive trajectories. If not considered, additional information will be lost, and the utility will be decreased. In this article, we propose a novel privacy-preserved trajectory data publishing method, i.e., IDF-OPT, which can reduce global least-information loss and guarantee strong individual privacy. Comprehensive experiments based on an actual trajectory publishing benchmark demonstrate that the proposed method maintains high practicability in trajectory data mining. |
| first_indexed | 2024-12-22T22:14:23Z |
| format | Article |
| id | doaj.art-adffa5c5e6f141df901890011848baf7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T22:14:23Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-adffa5c5e6f141df901890011848baf72022-12-21T18:10:48ZengIEEEIEEE Access2169-35362021-01-0197421743810.1109/ACCESS.2020.30483949311790Risk-Aware Individual Trajectory Data Publishing With Differential PrivacyJianzhe Zhao0https://orcid.org/0000-0003-4492-5075Jie Mei1Stan Matwin2Yukai Su3Yuancheng Yang4Software College, Northeastern University, Shenyang, ChinaMicrosoft Corporation, Redmond, WA, USADepartment of Computer Science, Dalhousie University, Halifax, NS, CanadaSoftware College, Northeastern University, Shenyang, ChinaSoftware College, Northeastern University, Shenyang, ChinaLarge-scale spatiotemporal data mining has created valuable insights into managing key areas of society and the economy. It has encouraged data owners to release/publish trajectory datasets. However, the ill-informed publication of such valuable datasets may lead to serious privacy implications for individuals. Moreover, as a major goal of data protection, balancing privacy and utility remains a challenging problem due to the diversity of spatiotemporal data. However, the user dimension was not considered for traditional frameworks, which limits the application at the global level as opposed to the user level. Many researchers overcome this issue by assuming that a user in the dataset generates only one trajectory. Actually, a user always generates multiple and repetitive trajectories during observation. Only considering one trajectory for one user may cause insufficient privacy protection at the trajectory level alone, as a user's privacy can be manifested in many trajectories collectively. In addition, it demonstrates strong user correlation when using multiple and repetitive trajectories. If not considered, additional information will be lost, and the utility will be decreased. In this article, we propose a novel privacy-preserved trajectory data publishing method, i.e., IDF-OPT, which can reduce global least-information loss and guarantee strong individual privacy. Comprehensive experiments based on an actual trajectory publishing benchmark demonstrate that the proposed method maintains high practicability in trajectory data mining.https://ieeexplore.ieee.org/document/9311790/Differential privacytrajectory data publishingdata correlationutility optimization |
| spellingShingle | Jianzhe Zhao Jie Mei Stan Matwin Yukai Su Yuancheng Yang Risk-Aware Individual Trajectory Data Publishing With Differential Privacy IEEE Access Differential privacy trajectory data publishing data correlation utility optimization |
| title | Risk-Aware Individual Trajectory Data Publishing With Differential Privacy |
| title_full | Risk-Aware Individual Trajectory Data Publishing With Differential Privacy |
| title_fullStr | Risk-Aware Individual Trajectory Data Publishing With Differential Privacy |
| title_full_unstemmed | Risk-Aware Individual Trajectory Data Publishing With Differential Privacy |
| title_short | Risk-Aware Individual Trajectory Data Publishing With Differential Privacy |
| title_sort | risk aware individual trajectory data publishing with differential privacy |
| topic | Differential privacy trajectory data publishing data correlation utility optimization |
| url | https://ieeexplore.ieee.org/document/9311790/ |
| work_keys_str_mv | AT jianzhezhao riskawareindividualtrajectorydatapublishingwithdifferentialprivacy AT jiemei riskawareindividualtrajectorydatapublishingwithdifferentialprivacy AT stanmatwin riskawareindividualtrajectorydatapublishingwithdifferentialprivacy AT yukaisu riskawareindividualtrajectorydatapublishingwithdifferentialprivacy AT yuanchengyang riskawareindividualtrajectorydatapublishingwithdifferentialprivacy |