Communications of the ACM
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
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In privacy-preserving data mining, there is a need to consider on-line data collection applications in a client-server-to-user (CS2U) model, in which a trusted server can help clients create and disseminate anonymous data. Existing privacy-preserving data publishing (PPDP) and privacy-preserving data collection (PPDC) methods do not sufficiently address the needs of these applications. In this paper, we present a novel PPDC method that lets respondents (clients) use generalization to create anonymous data in the CS2U model. Generalization is widely used for PPDP but has not been used for PPDC. We propose a new probabilistic privacy measure to model a distribution attack and use it to define the respondent's problem (RP) for finding an optimal anonymous tuple. We show that RP is NP-hard and present a heuristic algorithm for it. Our method is compared with a number of existing PPDC and PPDP methods in experiments based on two UCI datasets and two utility measures. Preliminary results show that our method can better protect against the distribution attack and provide good balance between privacy and data utility.