Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on 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
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Data privacy protection in multi-party clustering
Data & Knowledge Engineering
Optimization for MASK Scheme in Privacy Preserving Data Mining for Association Rules
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving clustering for multi-party
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Probabilistic frequent pattern growth for itemset mining in uncertain databases
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
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Privacy concerns have become an important issue in data mining. A popular way to preserve privacy is to randomize the dataset to be mined in a systematic way and mine the randomized dataset instead. On the other hand, people usually have different privacy concerns for different attributes in data. E.g., in survey data, the sensitivity of questions varies. Appropriate use of this information can lead to more accurate data mining results. However, this information has not been fully utilized by many privacy preserving association rule mining algorithms.In this paper, we generalize the privacy preserving association rule mining problem by allowing different attributes to have different levels of privacy, that is, using different randomization factors for values of different attributes in the randomization process. We also propose an efficient algorithm RE (Recursive Estimation) to estimate the support of itemsets under this framework. Both theoretical and empirical results show that the use of non-uniform randomization factors improves the accuracy of the support estimates, compared to the use of one conservative randomization factor.