The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Hiding the presence of individuals from shared databases
Abstract
Advances in information technology, and its use in research, are increasing both the need for anonymized data and the risks of poor anonymization. We present a metric, δ-presence, that clearly links the quality of anonymization to the risk posed by inadequate anonymization. We show that existing anonymization techniques are inappropriate for situations where δ-presence is a good metric (specifically, where knowing an individual is in the database poses a privacy risk), and present algorithms for effectively anonymizing to meet δ-presence. The algorithms are evaluated in the context of a real-world scenario, demonstrating practical applicability of the approach.
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Citations
Cites
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
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
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
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
Transforming data to satisfy privacy constraints
Iyengar, V. S. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775089 · Cited by 790
Anatomy: simple and effective privacy preservation
Xiao, X., & Tao, Y. (2006) · Very Large Data Bases · doi:10.5555/1182635.1164141 · Cited by 642
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
“Different principles of quantitative data anonymization have been developed, such as k-anonymity [288], (c,k )-safety [236], and δ -presence [253].”
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
How Much Is Enough? Choosing ε for Differential Privacy
Lee, J., & Clifton, C. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24861-0_22 · Cited by 201
Self-citationA Survey on Differentially Private Machine Learning [Review Article]
Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2020.2976185 · Cited by 115
Metadata
- Authors
- Nergiz, M. E., Atzori, M., & Clifton, C.
- Venue
- Proceedings of the 2007 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 665-676
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2139864694
- Cited by
- 304
- References
- 17
- FWCI
- 16.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nergiz, M. E., Atzori, M., & Clifton, C. (2007). Hiding the presence of individuals from shared databases. Proceedings of the 2007 ACM SIGMOD international conference on Management of data. https://doi.org/10.1145/1247480.1247554
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