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
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)
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
| Hi-index | 12.06 |
We propose here an efficient data mining algorithm to sanitize informative association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicting item, an informative association rule set [Li, J., Shen, Hong, & Topor, R. (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. Several approaches to sanitize informative association rules from static databases have been proposed [Wang, S. L., Parikh, B., & Jafari, A. (2007). Hiding informative association rule sets. Expert Systems with Applications, 33(2), 316-323 and Wang, S. L., Maskey, R., Jafari, A., & Hong, T. P. (2007). Efficient sanitization of informative association rules. Expert Systems with Applications. doi: 10.1016/j.eswa.2007.07.039]. However, frequent updates to the database may require repeated sanitization of original database and added data sets. The efforts of previous sanitization are not utilized in these approaches. In this work, we propose using pattern inversion tree to store the added data set in one database scan. It is then sanitized and merged to the original sanitized database. Various characteristics of the proposed algorithm are analyzed. Numerical experiments and running time analyses show that the proposed approach out performs the direct sanitization approach on original and added data sets, with similar side effects.