Personalized Privacy‐Preserving Trajectory Data Publishing
| Autor: | Qiwei Lu, Yan Xiong, Wenchao Huang, Caimei Wang, Huihua Xia, Xudong Gong |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Scheme (programming language)
Information privacy business.industry Computer science Applied Mathematics Mobile computing 020206 networking & telecommunications 02 engineering and technology Data publishing Computer security computer.software_genre Popularity Personalization 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Electronic publishing Electrical and Electronic Engineering business computer computer.programming_language |
| Zdroj: | Chinese Journal of Electronics. 26:285-291 |
| ISSN: | 2075-5597 1022-4653 |
| DOI: | 10.1049/cje.2017.01.024 |
| Popis: | Due to the popularity of mobile internet and location-aware devices, there is an explosion of location and trajectory data of moving objects. A few proposals have been proposed for privacy preserving trajectory data publishing, and most of them assume the attacks with the same adversarial background knowledge. In practice, different users have different privacy requirements. Such non-personalized privacy assumption does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. We study the personalized trajectory k-anonymity criterion for trajectory data publication. Specifically, we explore and propose an overall framework which provides privacy preserving services based on users' personal privacy requests, including trajectory clustering, editing and publication. We demonstrate the efficiency and effectiveness of our scheme through experiments on real world dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text