Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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 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)
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
A Max-Min Approach for Hiding Frequent Itemsets
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Preserving Private Knowledge in Frequent Pattern Mining
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Hiding sensitive knowledge without side effects
Knowledge and Information Systems
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Information Sciences: an International Journal
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
| Hi-index | 12.05 |
Privacy preserving data mining (PPDM) is a popular topic in the research community. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on privacy preserving utility mining (PPUM) and presents two novel algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries cannot mine them from the modified database. The work also minimizes the impact on the sanitized database of hiding sensitive itemsets. The experimental results show that HHUIF achieves lower miss costs than MSICF on two synthetic datasets. On the other hand, MSICF generally has a lower difference ratio than HHUIF between original and sanitized databases.