Anonymizing transaction databases for publication
Abstract
This paper considers the problem of publishing "transaction data" for research purposes. Each transaction is an arbitrary set of items chosen from a large universe. Detailed transaction data provides an electronic image of one's life. This has two implications. One, transaction data are excellent candidates for data mining research. Two, use of transaction data would raise serious concerns over individual privacy. Therefore, before transaction data is released for data mining, it must be made anonymous so that data subjects cannot be re-identified. The challenge is that transaction data has no structure and can be extremely high dimensional. Traditional anonymization methods lose too much information on such data. To date, there has been no satisfactory privacy notion and solution proposed for anonymizing transaction data. This paper proposes one way to address this issue.
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Cloud-based data sharing difficulty lies in how much trust users can place on cloud service providers, due to lack of transparency and loss of data control by users in cloud environments.
“Considering the fact that users and cloud providers usually belong to different administrative or security domains, the difficulty of cloud-based data sharing lies in how much trust users can place on cloud service providers.”
From A review of secure and privacy-preserving medical data sharing (2019) · cites this paper as [19]
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Privacy-preserving data publishing: A survey of recent developments
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A review of secure and privacy-preserving medical data sharing
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- Authors
- Xu, Y., Wang, K., Fu, A. W.-C., & Yu, P. S.
- Affiliations
- Simon Fraser University (CA)
- Chinese University of Hong Kong (HK)
- University of Illinois Chicago (US)
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 767-775
- OpenAlex
- W2000646855
- Cited by
- 187
- References
- 21
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 7 · 2017: 8 · 2016: 8 · 2015: 13 · 2014: 26 · 2013: 14 · 2012: 25
- FWCI
- 14.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- privacy-preserving data publishing, re-identification, high-dimensional data, data mining, transactional databases
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