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Conference Paper: Angels and Daemons: Is more Knowledge better than less Privacy? An Empirical Study on a K-anonymized openly available Dataset
Title | Angels and Daemons: Is more Knowledge better than less Privacy? An Empirical Study on a K-anonymized openly available Dataset |
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Authors | |
Keywords | Data privacy Information security/privacy Privacy/information privacy Open database |
Issue Date | 2017 |
Publisher | Association for Information Systems. |
Citation | International Conference on Information Systems (ICIS 2017), Seoul, South Korea, 10-13 December 2017. In ICIS 2017 Proceedings How to Cite? |
Abstract | Many organizations are starting to make datasets, such as customer review data and service
usage logs. To protect the privacy of involved individuals, these datasets are usually
pseudonymized or anonymized before they are released. A method called k-anonymization is
widely used in such open datasets. Recent literature showed that this method, however, can be
unsafe and compromise individuals’ privacy. In this paper, we address this problem by
analyzing the New York Citi Bike dataset. Through our analyses, we show that given some
generalized and payload data, it is possible to recover other payload data of an individual in the
k-anonymized dataset. We also demonstrate that it is possible to achieve a high success rate in
re-identification of records. These findings shed additional light on the weakness of the kanonymization
method, thus evidencing a trade-off between data availability and privacy
protection. We finally provide some implications for both academics and practitioners. |
Persistent Identifier | http://hdl.handle.net/10722/260899 |
DC Field | Value | Language |
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dc.contributor.author | Pennarola, F | - |
dc.contributor.author | Pistilli, L | - |
dc.contributor.author | Chau, MCL | - |
dc.date.accessioned | 2018-09-14T08:49:13Z | - |
dc.date.available | 2018-09-14T08:49:13Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | International Conference on Information Systems (ICIS 2017), Seoul, South Korea, 10-13 December 2017. In ICIS 2017 Proceedings | - |
dc.identifier.uri | http://hdl.handle.net/10722/260899 | - |
dc.description.abstract | Many organizations are starting to make datasets, such as customer review data and service usage logs. To protect the privacy of involved individuals, these datasets are usually pseudonymized or anonymized before they are released. A method called k-anonymization is widely used in such open datasets. Recent literature showed that this method, however, can be unsafe and compromise individuals’ privacy. In this paper, we address this problem by analyzing the New York Citi Bike dataset. Through our analyses, we show that given some generalized and payload data, it is possible to recover other payload data of an individual in the k-anonymized dataset. We also demonstrate that it is possible to achieve a high success rate in re-identification of records. These findings shed additional light on the weakness of the kanonymization method, thus evidencing a trade-off between data availability and privacy protection. We finally provide some implications for both academics and practitioners. | - |
dc.language | eng | - |
dc.publisher | Association for Information Systems. | - |
dc.relation.ispartof | ICIS 2017 Proceedings | - |
dc.subject | Data privacy | - |
dc.subject | Information security/privacy | - |
dc.subject | Privacy/information privacy | - |
dc.subject | Open database | - |
dc.title | Angels and Daemons: Is more Knowledge better than less Privacy? An Empirical Study on a K-anonymized openly available Dataset | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Chau, MCL: mchau@business.hku.hk | - |
dc.identifier.authority | Chau, MCL=rp01051 | - |
dc.identifier.hkuros | 291332 | - |
dc.publisher.place | Seoul, South Korea | - |