Dynamic Perfect Hashing: Upper and Lower Bounds
SIAM Journal on Computing
Data mining: concepts and techniques
Data mining: concepts and techniques
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Modern Information Retrieval
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Impact of Decision-Region Based Classification Mining Algorithms on Database Security
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
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
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Database Security-Concepts, Approaches, and Challenges
IEEE Transactions on Dependable and Secure Computing
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An integer programming approach for frequent itemset hiding
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
Dare to share: Protecting sensitive knowledge with data sanitization
Decision Support Systems
Hiding informative association rule sets
Expert Systems with Applications: An International Journal
Minimizing Information Loss and Preserving Privacy
Management Science
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Hiding collaborative recommendation association rules
Applied Intelligence
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
Preserving privacy in association rule mining with bloom filters
Journal of Intelligent Information Systems
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Efficient sanitization of informative association rules
Expert Systems with Applications: An International Journal
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
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
Protecting business intelligence and customer privacy while outsourcing data mining tasks
Knowledge and Information Systems
Maintenance of sanitizing informative association rules
Expert Systems with Applications: An International Journal
A Heuristic Data Reduction Approach for Associative Classification Rule Hiding
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Privacy preserving itemset mining through noisy items
Expert Systems with Applications: An International Journal
Data Confidentiality Versus Chase
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
HHUIF and MSICF: Novel algorithms for privacy preserving utility mining
Expert Systems with Applications: An International Journal
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
Target-based privacy preserving association rule mining
Proceedings of the 2011 ACM Symposium on Applied Computing
Two new techniques for hiding sensitive itemsets and their empirical evaluation
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Ensuring data security against knowledge discovery in distributed information systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
An effective approach for hiding sensitive knowledge in data publishing
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Mining association rules from distorted data for privacy preservation
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Hiding emerging patterns with local recoding generalization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Privacy preserving mining maximal frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Privacy-preserving frequent itemsets mining via secure collaborative framework
Security and Communication Networks
Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
Trends and research directions for privacy preserving approaches on the cloud
Proceedings of the 6th ACM India Computing Convention
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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One crucial aspect of privacy preserving frequent itemset mining is the fact that the mining process deals with a trade-off: privacy and accuracy, which are typically contradictory, and improving one usually incurs a cost in the other. One alternative to address this particular problem is to look for a balance between hiding restrictive patterns and disclosing nonrestrictive ones. In this paper, we propose a new framework for enforcing privacy in mining frequent itemsets. We combine, in a single framework, techniques for efficiently hiding restrictive patterns: a transaction retrieval engine relying on an inverted file and Boolean queries; and a set of algorithms to "sanitize" a database. In addition, we introduce performance measures for mining frequent itemsets that quantify the fraction of raining patterns which are preserved after sanitizing a database. We also report the results of a performance evaluation of our research prototype and an analysis of the results.