Visualising Personal Data Flows: Insights from a Case Study of Booking.com

Yuan, Haiyue, Boakes, Matthew, Ma, Xiao, Cao, Dongmei, Li, Shujun (2023) Visualising Personal Data Flows: Insights from a Case Study of Booking.com. In: Lecture Notes in Business Information Processing. Intelligent Information Systems: CAiSE Forum 2023, Zaragoza, Spain, June 12–16, 2023, Proceedings. 477. pp. 52-60. Springer Cham, Cham, Germany E-ISBN 978-3-031-34674-3. (doi:10.1007/978-3-031-34674-3_7) (KAR id:101789)

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Official URL: https://doi.org/10.1007/978-3-031-34674-3_7

Abstract

Commercial organisations are holding and processing an ever-increasing amount of personal data. Policies and laws are continually changing to require these companies to be more transparent regarding collection, storage, processing and sharing of this data. This paper reports our work of taking Booking.com as a case study to visualise personal data flows extracted from their privacy policy. By showcasing how the company shares its consumers’ personal data, we raise questions and extend discussions on the challenges and limitations of using privacy policies to inform online users about the true scale and the landscape of personal data flows. This case study can inform us about future research on more data flow-oriented privacy policy analysis and on the construction of a more comprehensive ontology on personal data flows in complicated business ecosystems.

Item Type:	Conference or workshop item (Paper)
DOI/Identification number:	10.1007/978-3-031-34674-3_7
Uncontrolled keywords:	Personal data, Data flow, Privacy policy, Privacy, Data sharing, Travel
Subjects:	H Social Sciences > HF Commerce > HF5548.32 E-commerce Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5105 Data transmission systems > TK5105.5 Computer networks > TK5105.875.I57 Internet T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5105.888 World Wide Web
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing University-wide institutes > Institute of Cyber Security for Society
Funders:	Engineering and Physical Sciences Research Council (https://ror.org/0439y7842)
Depositing User:	Shujun Li
Date Deposited:	21 Jun 2023 17:45 UTC
Last Modified:	21 Jun 2023 17:45 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/101789 (The current URI for this page, for reference purposes)
Yuan, Haiyue:	https://orcid.org/0000-0001-6084-6719
Boakes, Matthew:	https://orcid.org/0000-0002-9377-6240
Cao, Dongmei:	https://orcid.org/0000-0002-2614-3726
Li, Shujun:	https://orcid.org/0000-0001-5628-7328

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