An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing

• Main Authors: A S M Touhidul Hasan, Qingshan Jiang, Chengming Li
• Format: Article
• Language: English
• Published: MDPI AG 2017-10-01
• Series: Future Internet
• Subjects: bike sharing; identity disclosure; linking attacks; data publishing; privacy preservation
• Online Access: https://www.mdpi.com/1999-5903/9/4/65

Bike sharing programs are eco-friendly transportation systems that are widespread in smart city environments. In this paper, we study the problem of privacy-preserving bike sharing microdata publishing. Bike sharing systems collect visiting information along with user identity and make it public by removing the user identity. Even after excluding user identification, the published bike sharing dataset will not be protected against privacy disclosure risks. An adversary may arrange published datasets based on bike’s visiting information to breach a user’s privacy. In this paper, we propose a grouping based anonymization method to protect published bike sharing dataset from linking attacks. The proposed Grouping method ensures that the published bike sharing microdata will be protected from disclosure risks. Experimental results show that our approach can protect user privacy in the released datasets from disclosure risks and can keep more data utility compared with existing methods.