Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
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
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Privacy: A Machine Learning View
IEEE Transactions on Knowledge and Data Engineering
Hiding Sensitive Patterns in Association Rules Mining
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Preserving Privacy by De-Identifying Face Images
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A reconstruction-based algorithm for classification rules hiding
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
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
Data Mining and Knowledge Discovery
Minimizing Information Loss and Preserving Privacy
Management Science
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
Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data
Information Systems Research
Hiding collaborative recommendation association rules
Applied Intelligence
Safely delegating data mining tasks
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
The motivation and proposition of a privacy-enhancing architecture for operational databases
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
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
TFRP: An efficient microaggregation algorithm for statistical disclosure control
Journal of Systems and Software
Preserving privacy in association rule mining with bloom filters
Journal of Intelligent Information Systems
Guest editorial: Recent advances in preserving privacy when mining data
Data & Knowledge Engineering
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
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
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
Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Protecting business intelligence and customer privacy while outsourcing data mining tasks
Knowledge and Information Systems
FRAPP: a framework for high-accuracy privacy-preserving mining
Data Mining and Knowledge Discovery
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
International Journal of Computer Applications in Technology
Privacy preservation of aggregates in hidden databases: why and how?
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Hiding Predictive Association Rules on Horizontally Distributed Data
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Privacy risks in health databases from aggregate disclosure
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Reconstructing Data Perturbed by Random Projections When the Mixing Matrix Is Known
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A novel approach for privacy mining of generic basic association rules
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
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
Privacy-preserving discovery of frequent patterns in time series
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Privacy-preserving frequent pattern sharing
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
A cost-efficient and versatile sanitizing algorithm by using a greedy approach
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Privacy-preserving publishing microdata with full functional dependencies
Data & Knowledge Engineering
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Sanitization of databases for refined privacy trade-offs
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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
Aggregate suppression for enterprise search engines
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
An enhanced secure preserving for pre-processed data using DMI and PCRBAC algorithm
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Collusion-Free Privacy Preserving Data Mining
International Journal of Intelligent Information Technologies
Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree
International Journal of Information Security and Privacy
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
Association rule hiding in risk management for retail supply chain collaboration
Computers in Industry
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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Data products (macrodata or tabular data and microdata or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently.This paper, deals with the problem of limiting disclosure of sensitive rules. In particular, it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other, non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database ``frequently enough'' and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem.