Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Reducing the bandwidth of sparse symmetric matrices
ACM '69 Proceedings of the 1969 24th national conference
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for High-Accuracy Privacy-Preserving Mining
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Reducing the Total Bandwidth of a Sparse Unsymmetric Matrix
SIAM Journal on Matrix Analysis and Applications
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
OptRR: Optimizing Randomized Response Schemes for Privacy-Preserving Data Mining
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On Anti-Corruption Privacy Preserving Publication
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Modeling and Integrating Background Knowledge in Data Anonymization
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Optimal random perturbation at multiple privacy levels
Proceedings of the VLDB Endowment
Limiting link disclosure in social network analysis through subgraph-wise perturbation
Proceedings of the 15th International Conference on Extending Database Technology
Publishing microdata with a robust privacy guarantee
Proceedings of the VLDB Endowment
Negotiation-based privacy preservation scheme in internet of things platform
Proceedings of the First International Conference on Security of Internet of Things
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Random perturbation is a promising technique for privacy preserving data mining. It retains an original sensitive value with a certain probability and replaces it with a random value from the domain with the remaining probability. If the replacing value is chosen from a large domain, the retention probability must be small to protect privacy. For this reason, previous randomization-based approaches have poor utility. In this paper, we propose an alternative way to randomize sensitive values, called small domain randomization. First, we partition the given table into sub-tables that have smaller domains of sensitive values. Then, we randomize the sensitive values within each sub-table independently. Since each sub-table has a smaller domain, a larger retention probability is permitted. We propose this approach as an alternative to classical partition-based approaches to privacy preserving data publishing. There are two key issues: ensure the published sub-tables do not disclose more private information than what is permitted on the original table, and partition the table so that utility is maximized. We present an effective solution.