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
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
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Our aim is to generate a privacy preserving micro data table for release, from the original table. A Privacy Preserving Transformation (APPT) tool developed, transforms both the numerical and nominal sensitive attributes to preserve privacy while maintaining the information and hence the utility. Any type of mining tasks can be performed on the released Micro data table (transformed table) without any modification in the algorithms. The data mining results obtained do not disclose sensitive information but the data owners and authorized persons can be allowed to interpret the actual sensitive values. The tool can be used to merge the partitioned data, whether it is horizontally and vertically partitioned. Experiments performed using Real Adult dataset prove that the Released table preserves privacy which is as good as any other popular anonymization techniques but reducing information loss. The tool does not require any domain expert. But the cost we pay for the privacy is the high security for the centralized server.