Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Elements of information theory
Elements of information theory
Parsimonious downgrading and decision trees applied to the inference problem
Proceedings of the 1998 workshop on New security paradigms
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
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
Privacy Preserving Association Rule Mining
RIDE '02 Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02)
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Privacy sensitive distributed data mining from multi-party data
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Suppressing microdata to prevent classification based inference
The VLDB Journal — The International Journal on Very Large Data Bases
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Enterprises have been collecting data for many reasons including better customer relationship management, and high-level decision making. Public safety was another motivation for large-scale data collection efforts initiated by government agencies. However, such widespread data collection efforts coupled with powerful data analysis tools raised concerns about privacy. This is due to the fact that collected data may contain confidential information, or it can be used to infer confidential information. One method to ensure privacy is to selectively hide confidential data values from the data set to be disclosed. However, with data mining technology it is now possible for an adversary to predict the hidden data values, which is another threat to privacy. In this paper we concentrate on probabilistic classification, which is a specific data mining technique widely used for prediction purposes, and propose methods for downgrading probabilistic classification models in order to block the inference of hidden microdata values.