Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Security Constraint Processing in a Multilevel Secure Distributed Database Management System
IEEE Transactions on Knowledge and Data Engineering
Inference in MLS Database Systems
IEEE Transactions on Knowledge and Data Engineering
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
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
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
To do or not to do: the dilemma of disclosing anonymized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Suppressing Data Sets to Prevent Discovery of Association Rules
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On privacy preservation against adversarial data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A reconstruction-based algorithm for classification rules hiding
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining
IEEE Transactions on Knowledge and Data Engineering
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
Customer-oriented catalog segmentation: effective solution approaches
Decision Support Systems
Minimizing Information Loss and Preserving Privacy
Management Science
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
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
MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining
Advanced Engineering Informatics
Privacy-preserving distributed association rule mining via semi-trusted mixer
Data & Knowledge Engineering
Efficient algorithms for distortion and blocking techniques in association rule hiding
Distributed and Parallel Databases
Privacy preserving clustering on horizontally partitioned 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
Efficient sanitization of informative association rules
Expert Systems with Applications: An International Journal
Efficient algorithms for incremental Web log mining with dynamic thresholds
The VLDB Journal — The International Journal on Very Large Data Bases
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Hiding Sensitive Associative Classification Rule by Data Reduction
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
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
Privately detecting bursts in streaming, distributed time series data
Data & Knowledge Engineering
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
(α, k)-anonymous data publishing
Journal of Intelligent Information Systems
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 framework for safely publishing communication traces
Proceedings of the 18th ACM conference on Information and knowledge management
Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining
Decision Support Systems
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Hiding co-occurring sensitive patterns in progressive databases
Proceedings of the 2010 EDBT/ICDT Workshops
Data mining for discrimination discovery
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
k-anonymization without Q-S associations
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Peer-to-peer data mining, privacy issues, and games
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
A three-dimensional conceptual framework for database privacy
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
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
Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm
Expert Systems with Applications: An International Journal
Publishing time-series data under preservation of privacy and distance orders
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Integrating induction and deduction for finding evidence of discrimination
Artificial Intelligence and Law
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
Local and global recoding methods for anonymizing set-valued data
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving publishing microdata with full functional dependencies
Data & Knowledge Engineering
Measuring side effects of rule hiding
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
An improved EDP algorithm to privacy protection in data mining
BI'11 Proceedings of the 2011 international conference on Brain informatics
An attacker's view of distance preserving maps for privacy preserving data mining
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Privacy leakage in multi-relational learning via unwanted classification models
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
Can attackers learn from samples?
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
Suppressing microdata to prevent probabilistic classification based inference
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Hiding classification rules for data sharing with privacy preservation
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Privacy preserving data mining services on the web
TrustBus'05 Proceedings of the Second international conference on Trust, Privacy, and Security in Digital Business
A graph enrichment based clustering over vertically partitioned data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Aggregate suppression for enterprise search engines
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
Breaching Euclidean distance-preserving data perturbation using few known inputs
Data & Knowledge Engineering
On differentially private frequent itemset mining
Proceedings of the VLDB Endowment
Collusion-Free Privacy Preserving Data Mining
International Journal of Intelligent Information Technologies
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
Association rule hiding in risk management for retail supply chain collaboration
Computers in Industry
Pufferfish: A framework for mathematical privacy definitions
ACM Transactions on Database Systems (TODS)
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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Abstract--Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that 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, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database.