Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining (Invited talk. Abstract only): crossing the Chasm
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
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
An Entropy-Based Framework for Database Inference
IH '99 Proceedings of the Third International Workshop on Information Hiding
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
A methodology for hiding knowledge in databases
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
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
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
A quantitative and qualitative ANALYSIS of blocking in association rule hiding
Proceedings of the 2004 ACM workshop on Privacy in the electronic society
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
Hiding informative association rule sets
Expert Systems with Applications: An International Journal
Privacy preserving itemset mining through fake transactions
Proceedings of the 2007 ACM symposium on Applied computing
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
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
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
Optimization for MASK Scheme in Privacy Preserving Data Mining for Association Rules
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Privacy Preserving Market Basket Data Analysis
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
FRAPP: a framework for high-accuracy privacy-preserving mining
Data Mining and Knowledge Discovery
Privacy preserving itemset mining through noisy items
Expert Systems with Applications: An International Journal
Employing PRBAC for privacy preserving data publishing
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Inference in distributed data clustering
Engineering Applications of Artificial Intelligence
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
Measuring side effects of rule hiding
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
A heuristic data-sanitization approach based on TF-IDF
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Optimized two party privacy preserving association rule mining using fully homomorphic encryption
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
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
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
Inference on distributed data clustering
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Association rule-based feature selection method for Alzheimer's disease diagnosis
Expert Systems with Applications: An International Journal
Privacy-preserving frequent itemsets mining via secure collaborative framework
Security and Communication Networks
Functional brain image classification using association rules defined over discriminant regions
Pattern Recognition Letters
Anonymizing set-valued data by nonreciprocal recoding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A novel framework for preserving privacy of data using correlation analysis
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Expert Systems with Applications: An International Journal
Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree
International Journal of Information Security and Privacy
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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Large repositories of data contain sensitive information which must be protected against unauthorized access. Recent advances, in data mining and machine learning algorithms, have increased the disclosure risks one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. In this paper, we investigate confidentiality issues of a broad category of rules, which are called association rules. If the disclosure risk of some of these rules are above a certain privacy threshold, those rules must be characterized as sensitive. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferencing sensitive data, or they may provide business competitors with an advantage.