On t-closeness with KL-divergence and semantic privacy
Authors: 
Chaofeng Sha, Yi Li, Aoying ZhouAuthors Info & Claims
Pages 153 - 167
Abstract
In this paper, we study how to sanitize the publishing data with sensitive attribute to achieve t-closeness and δ-disclosure privacy under Incognito framework. t-closeness is a privacy measure proposed to account for skewness attack and similarity attack, which are limitations of l-diversity. Under the t-closeness model, the distance between the privacy attribute distribution and the global one should be under the threshold t. Whereas semantic privacy (δ-disclosure privacy) is used to measure the incremental information gain from the anonymized tables. We use the Kullback-Leibler divergence to measure the distance between distributions and discuss the properties of the semantic privacy. We also study the relationship between t-closeness with KL-divergence and semantic privacy, and show that t-closeness with KL-divergence and δ-disclosure privacy satisfy the generalization property and the subset property, which entail us to use the Incognito algorithm. Experiments demonstrate the efficiency and effectiveness of our approaches.
References
[1]
http://www.cs.cornell.edu/database/privacy/code/l-diversity/ incognito-ldiversity.tgz
[2]
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Approximation algorithms for k-anonymity. Journal of Privacy Technology (2005).
[3]
Bayardo, R., Agrawal, R.: Data privacy through optimal k-anonymization. In: ICDE 2005 (2005).
[4]
Brickell, J., Shmatikov, V.: The cost of privacy: Destruction of data-mining utility in anonymized data publishing. In: KDD 2008 (2008).
[5]
Bu, Y., Fu, A., Wong, R., Chen, L., Li, J.: Privacy preserving serial data publishing by role composition. In: VLDB 2008 (2008).
[6]
Cover, T., Thomas, J.: Elements of Information Theory. Wiley Interscience, Hoboken (1991).
[7]
Ghinita, G., Karras, P., Kalnis, P.,Mamoulis, N.: Fast data anonymization with low information loss. In: VLDB 2007 (2007).
[8]
Kifer, D., Gehrke, J.: Injecting utility into anonymized datasets. In: SIGMOD (2006).
[9]
LeFevre, K., DeWitt, D., Ramakrishnan, R.: Incognito: Efficient full-domain k-anonymity. In: SIGMOD (2005).
[10]
LeFevre, K., DeWitt, D., Ramakrishnan, R.:Workload-aware anonymization. In: KDD 2006 (2006).
[11]
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE 2007 (2007).
[12]
Li, T., Li, N.: On the tradeoff between privacy and utility in data publishing. In: KDD 2009 (2009).
[13]
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: ICDE 2006 (2006).
[14]
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: PODS 2004 (2004).
[15]
Park, H., Shim, K.: Approximate algorithms for k-anonymity. In: SIGMOD 2007 (2007).
[16]
Samarati, P.: Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13, 1010-1027 (2001).
[17]
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10 (2002).
[18]
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10 (2002).
[19]
Xiao, X., Tao, Y.: Anatomy: Simple and effective privacy preservation. In: VLDB 2006 (2006).
[20]
Xiao, X., Tao, Y.: Personalized privacy preservation. In: SIGMOD 2006 (2006).
[21]
Xiao, X., Tao, Y.: m-invariance: Towards privacy preserving re-publication of dynamic datasets. In: SIGMOD 2007 (2007).
[22]
Xiao, X., Tao, Y.: Dynamic anonymization: Accurate statistical analysis with privacy preservation. In: SIGMOD 2008 (2008).
- On t-closeness with KL-divergence and semantic privacy
Recommendations
From t-closeness to differential privacy and vice versa in data anonymization
k-anonymity and ε-differential privacy are two mainstream privacy models, the former introduced to anonymize data sets and the latter to limit the knowledge gain that results from including one individual in the data set. Whereas basic k-anonymity only ...
t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation
Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of ...
Closeness: A New Privacy Measure for Data Publishing
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
April 2010
493 pages
ISBN:3642120970
Sponsors
- NIMS: National Institute for Materials Science
- BeaconIT
- National Institute of Advanced Industrial Science and Technology
- KDDI R&D Laboratories Inc.
- Mitsubishi Electric Corporation
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 April 2010
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Jan 2025