The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Privacy-preserving anonymization of set-valued data
Abstract
In this paper we study the problem of protecting privacy in the publication of set-valued data. Consider a collection of transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike most previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the point of view of the adversary. We define a new version of the k -anonymity guarantee, the k m -anonymity, to limit the effects of the data dimensionality and we propose efficient algorithms to transform the database. Our anonymization model relies on generalization instead of suppression, which is the most common practice in related works on such data. We develop an algorithm which finds the optimal solution, however, at a high cost which makes it inapplicable for large, realistic problems. Then, we propose two greedy heuristics, which scale much better and in most of the cases find a solution close to the optimal. The proposed algorithms are experimentally evaluated using real datasets.
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Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335372 · Cited by 3,216
L-diversity: privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006) · 22nd International Conference on Data Engineering (ICDE'06) · doi:10.1109/icde.2006.1 · Cited by 2,362
Protecting respondents identities in microdata release
Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,324
Data Privacy through Optimal k-Anonymization
Bayardo, R. J., & Agrawal, R. (2005) · 21st International Conference on Data Engineering (ICDE'05) · doi:10.1109/icde.2005.42 · Cited by 1,131
Mondrian Multidimensional K-Anonymity
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2006) · 22nd International Conference on Data Engineering (ICDE'06) · doi:10.1109/icde.2006.101 · Cited by 1,124
Incognito: efficient full-domain K-anonymity
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2005) · Proceedings of the 2005 ACM SIGMOD international conference on Management of data · doi:10.1145/1066157.1066164 · Cited by 1,070
On the complexity of optimal K-anonymity
Meyerson, A., & Williams, R. (2004) · Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/1055558.1055591 · Cited by 821
Cited by
Privacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
“Specific DA methods have also been designed for relational data [262], set-valued data [151, 320], and image data [97, 239].”
BackgroundSlicing: A New Approach for Privacy Preserving Data Publishing
Li, T., Li, N., Zhang, J., & Molloy, I. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.236 · Cited by 312
A review of secure and privacy-preserving medical data sharing
Jin, H., Luo, Y., Li, P., & Mathew, J. (2019) · IEEE Access · doi:10.1109/access.2019.2916503 · Cited by 293
C‐sanitized: A privacy model for document redaction and sanitization
Sánchez, D., & Batet, M. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23363 · Cited by 80
Metadata
- Authors
- Terrovitis, M., Mamoulis, N., & Kalnis, P.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2008-08
- Volume
- 1
- Issue
- 1
- Pages
- 115-125
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2123453946
- Cited by
- 326
- References
- 24
- FWCI
- 34.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Terrovitis, M., Mamoulis, N., & Kalnis, P. (2008). Privacy-preserving anonymization of set-valued data. Proceedings of the VLDB Endowment. https://doi.org/10.14778/1453856.1453874
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