Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
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
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Mining the Smallest Association Rule Set for Predictions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Protecting Against Data Mining through Samples
Proceedings of the IFIP WG 11.3 Thirteenth International Conference on Database Security: Research Advances in Database and Information Systems Security
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Randomization in privacy preserving data mining
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
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
Association Analysis with One Scan of Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Privacy preserving frequent itemset mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Hiding informative association rule sets
Expert Systems with Applications: An International Journal
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Hi-index | 12.05 |
Recent development in privacy-preserving data mining has proposed many efficient and practical techniques for hiding sensitive patterns or information from been discovered by data mining algorithms. In hiding association rules, current approaches require hidden rules or patterns to be given in advance. In addition, for Apriori algorithm based techniques [Verykios, V., Elmagarmid, A., Bertino, E., Saygin, Y., & Dasseni, E. (2004). Association rules hiding. IEEE Transactions on Knowledge and Data Engineering, 16(4) 434-447], multiple scanning of the entire database is required. For direct sanitization of itemsets from transaction techniques [Oliveira, S., & Zaiane, O. (2003). An efficient on-scan sanitization for improving the balance between privacy and knowledge discovery. Technical report TR 03-15, Department of Computing Science, University of Alberta, Canada], one scanning of each window in the database is processed independently. However, the accumulated information among windows is not considered. In this work, we propose an efficient one database scanning sanitization algorithm to sanitize informative association rules. For a given predicting item, an informative association rule set [Li, Jiuyong, Shen, Hong, & Topor, Rodney. (2001). Mining the smallest association rule set for predictions, In Proceedings of the 2001 IEEE international conference on data mining (pp. 361-368)] is the smallest association rule set that makes the same prediction as the entire association rule set by confidence priority. A new data structure called pattern-inversion tree is proposed to store related information so that only one scan of database is required. The pre-process of finding these informative association rules can be integrated into the sanitization process. Numerical experiments show that the performance of the proposed algorithm is more efficient than previous algorithms with similar side effects. Running time complexity of the algorithm is presented and compared to similar algorithm with better complexity.