A General Additive Data Perturbation Method for Database Security
Management Science
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Informational privacy, data mining, and theInternet
Ethics and Information Technology
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
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
Data mining, national security, privacy and civil liberties
ACM SIGKDD Explorations Newsletter
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
A methodology for hiding knowledge in databases
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
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
Hiding Sensitive Patterns in Association Rules Mining
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Privacy-Preserving Data Mining: Why, How, and When
IEEE Security and Privacy
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Towards low-perturbation anonymity preserving pattern discovery
Proceedings of the 2006 ACM symposium on Applied computing
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining
Advanced Engineering Informatics
Efficient algorithms for distortion and blocking techniques in association rule hiding
Distributed and Parallel Databases
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Pushing Frequency Constraint to Utility Mining Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Privacy Preserving Data Mining Research: Current Status and Key Issues
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
HHUIF and MSICF: Novel algorithms for privacy preserving utility mining
Expert Systems with Applications: An International Journal
Weak k-anonymity: a low-distortion model for protecting privacy
ISC'06 Proceedings of the 9th international conference on Information Security
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Privacy preserving data mining is a vibrant area in data mining. The sharing of data between the organizations is found to be beneficial for business growth. However, privacy policies and threats prevent the data owners from sharing the data for mining. The current data sanitization approaches focus on hiding either frequent itemsets or utility itemsets separately. This paper proposes to study the problem of hiding the sensitive utility and frequent itemsets. To resolve this problem, two effective data sanitization algorithms MSMU and MCRSU are presented to hide the sensitive utility and frequent itemsets in the modified database. While hiding the sensitive itemsets, the algorithms sanitize the database with minimum impact on the non-sensitive itemsets. To accomplish this, MSMU is devised to identify the victim items with minimum support and maximum utility whereas MCRSU uses conflict ratio. Results from the computational experiments on the synthetic and real datasets indicate that MCRSU algorithm is more effective than MSMU in minimizing the non-sensitive itemsets affected as well as maintaining data quality in the sanitized database.