Utility-Driven k-Anonymization of Public Transport User Data

IEEE Access

Anwitaman Datta¹  Iva Bojic²  Bhawani Shanker Bhati³  Jordan Ivanchev³  David Eckhoff³ 

[1] School of Computer Science and Engineering, Nanyang Technological University, Singapore;Senseable City Laboratory, Singapore-MIT Alliance for Research and Technology, Singapore;TUMCREATE Ltd., Singapore;

关键词: Clustering;    k-anonymity;    privacy;    tap-in tap-out transportation data;    utility;   

DOI  :  10.1109/ACCESS.2021.3055505

来源: DOAJ

【 摘 要 】

In this article, we propose a k-anonymity approach that prioritizes the generalization of attributes based on their utility. We focus on transport data, which we consider a special case in which many or all attributes are quasi-identifiers (e.g., origin, destination, ride start time), as they allow correlation with easily observable auxiliary data. The novelty in our approach lies in introducing normalization techniques as well as distance and utility metrics that allow the consideration of not only numerical attributes but also categorical attributes by representing them in tree or graph form. The prioritization of the attributes in the generalization process is based on the attributes' utility and can further be influenced by either automatically or manually assigned attribute weights. We evaluate and compare different options for all components of our mechanism as well as present an extensive performance evaluation of our approach using real-world data. Lastly, we show in which cases suppression of records can counter-intuitively lead to higher data utility.

【 授权许可】

Unknown