Self-testing/correcting for polynomials and for approximate functions
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
On the hardness of computing the permanent of random matrices (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Highly resilient correctors for polynomials
Information Processing Letters
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
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
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
Journal of Computer and System Sciences - Special issue on PODS 2000
A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
Journal of the ACM (JACM)
Privacy: A Machine Learning View
IEEE Transactions on Knowledge and 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
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth 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
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
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
(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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Data & Knowledge Engineering
Information disclosure under realistic assumptions: privacy versus optimality
Proceedings of the 14th ACM conference on Computer and communications security
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
On the computational power of PP and (+)P
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Generating microdata with p-sensitive k-anonymity property
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Privacy Preserving Data Mining
Privacy Preserving Data Mining
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
A practical approximation algorithm for optimal k-anonymity
Data Mining and Knowledge Discovery
Secure distributed computation of anonymized views of shared databases
ACM Transactions on Database Systems (TODS)
Improving accuracy of classification models induced from anonymized datasets
Information Sciences: an International Journal
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We introduce a new model of k-type anonymity, called k-concealment, as an alternative to the well-known model of k-anonymity. This new model achieves similar privacy goals as k-anonymity: While in k-anonymity one generalizes the table records so that each one of them becomes equal to at least k-1 other records, when projected on the subset of quasi-identifiers, k-concealment proposes to generalize the table records so that each one of them becomes computationally-indistinguishable from at least k-1 others. As the new model extends that of k-anonymity, it offers higher utility. To motivate the new model and to lay the ground for its introduction, we first present three other models, called (1, k)-, (k, 1)-and (k, k)-anonymity which also extend k-anonymity. We characterize the interrelation between the four models and propose algorithms for anonymizing data according to them. Since k-anonymity, on its own, is insecure, as it may allow adversaries to learn the sensitive information of some individuals, it must be enhanced by a security measure such as p-sensitivity or l-diversity. We show how also k-concealment can be enhanced by such measures. We demonstrate the usefulness of our models and algorithms through extensive experiments.