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Proceedings of the 1st ACM conference on Electronic commerce
Data mining: concepts and techniques
Data mining: concepts and techniques
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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CHI '99 Extended Abstracts on Human Factors in Computing Systems
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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Privacy in electronic commerce and the economics of immediate gratification
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Sociotechnical Architecture for Online Privacy
IEEE Security and Privacy
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Vision paper: enabling privacy for the paranoids
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Information Sciences: an International Journal
Quantifying privacy violations
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
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This paper presents a concept hierarchy-based approach to privacy preserving data collection for data mining called the P-level model. The P-level model allows data providers to divulge information at any chosen privacy level (P-level), on any attribute. Data collected at a high P-level signifies divulgence at a higher conceptual level and thus ensures more privacy. Providing guarantees prior to release, such as satisfying k-anonymity (Samarati 2001; Sweeney 2002), can further protect the collected data set from privacy breaches due to linking the released data set with external data sets. However, the data mining process, which involves the integration of various data values, can constitute a privacy breach if combinations of attributes at certain P-levels result in the inference of knowledge that exists at a lower P-level. This paper describes the P-level reduction phenomenon and proposes methods to identify and control the occurrence of this privacy breach.