Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Scalarization and stability in vector optimization
Journal of Optimization Theory and Applications
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A crossover operator for the k- anonymity problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
(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
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Revisiting the uniqueness of simple demographics in the US population
Proceedings of the 5th ACM workshop on Privacy in electronic society
Capturing data usefulness and privacy protection in K-anonymisation
Proceedings of the 2007 ACM symposium on Applied computing
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
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
New Efficient Attacks on Statistical Disclosure Control Mechanisms
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation
ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
OptRR: Optimizing Randomized Response Schemes for Privacy-Preserving Data Mining
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On the Optimal Selection of k in the k-Anonymity Problem
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data 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
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Towards an anti-inference (k, ℓ)-anonymity model with value association rules
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Data anonymization techniques have received extensive attention in the privacy research community over the past several years. Various models of privacy preservation have been proposed: k-anonymity, ℓ-diversity and t-closeness, to name a few. An oft-cited drawback of these models is that there is considerable loss in data quality arising from the use of generalization and suppression techniques. Optimization attempts in this context have so far focused on maximizing the data utility for a pre-specified level of privacy. To determine if better privacy levels are obtainable with the same level of data utility, majority of the existing formulations require exhaustive analysis. Further, the data publisher's perspective is often missed in the process. The publisher wishes to maintain a given level of data utility since the data utility is the revenue earner and then maximize the level of privacy within acceptable limits. In this paper, we explore this privacy versus data quality trade-off as a multi-objective optimization problem. Our goal is to provide substantial information to a data publisher about the trade-offs available between the privacy level and the information content of an anonymized data set.