C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
Datafly: A System for Providing Anonymity in Medical Data
Proceedings of the IFIP TC11 WG11.3 Eleventh International Conference on Database Securty XI: Status and Prospects
The Design and Implementation of a Data Level Database Inference Detection System
Proceedings of the IFIP TC11 WG 11.3 Twelfth International Working Conference on Database Security XII: Status and Prospects
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Anonymizing sequential releases
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
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Privacy protection for RFID data
Proceedings of the 2009 ACM symposium on Applied Computing
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
(α, k)-anonymous data publishing
Journal of Intelligent Information Systems
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Data mining for discrimination discovery
ACM Transactions on Knowledge Discovery from Data (TKDD)
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Privacy protection on multiple sensitive attributes
ICICS'07 Proceedings of the 9th international conference on Information and communications security
Suppressing microdata to prevent classification based inference
The VLDB Journal — The International Journal on Very Large Data Bases
Versatile publishing for privacy preservation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A family of enhanced (L,α)-diversity models for privacy preserving data publishing
Future Generation Computer Systems
Integrating induction and deduction for finding evidence of discrimination
Artificial Intelligence and Law
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Anonymizing classification data using rough set theory
Knowledge-Based Systems
Pufferfish: A framework for mathematical privacy definitions
ACM Transactions on Database Systems (TODS)
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In this paper, we present a template-based privacy preservation to protect against the threats caused by data mining abilities. The problem has dual goals: preserve the information for a wanted classification analysis and limit the usefulness of unwanted sensitive inferences that may be derived from the data. Sensitive inferences are specified by a set of "privacy templates". Each template specifies the sensitive information to be protected, a set of identifying attributes, and the maximum association between the two. We show that suppressing the domain values is an effective way to eliminate sensitive inferences. For a large data set, finding an optimal suppression is hard, since it requires optimization over all suppressions. We present an approximate but scalable solution. We demonstrate the effectiveness of this approach on real life data sets.