Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/146652
Title: | Utility-driven k-anonymization of public transport user data | Authors: | Bhati, Bhawani Shanker Ivanchev, Jordan Bojic, Iva Datta, Anwitaman Eckhoff, David |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2021 | Source: | Bhati, B. S., Ivanchev, J., Bojic, I., Datta, A., & Eckhoff, D. (2021). Utility-driven k-anonymization of public transport user data. IEEE Access, 9, 23608-23623. doi:10.1109/ACCESS.2021.3055505 | Journal: | IEEE Access | Abstract: | In this paper, 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. | URI: | https://hdl.handle.net/10356/146652 | ISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2021.3055505 | Schools: | School of Computer Science and Engineering | Rights: | © 2021 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
09340178.pdf | 1.88 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
50
6
Updated on Mar 25, 2024
Web of ScienceTM
Citations
50
3
Updated on Oct 26, 2023
Page view(s)
256
Updated on Mar 29, 2024
Download(s) 50
107
Updated on Mar 29, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.