Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Security of random data perturbation methods
ACM Transactions on Database Systems (TODS)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-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
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
Inference in MLS Database Systems
IEEE Transactions on Knowledge and Data Engineering
Wizard: A Database Inference Analysis and Detection System
IEEE Transactions on Knowledge and Data Engineering
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Cardinality-Based Inference Control in Sum-Only Data Cubes
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
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
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Catalytic Inference Analysis: Detecting Inference Threats due to Knowledge Discovery
SP '97 Proceedings of the 1997 IEEE Symposium on Security and Privacy
A formal analysis of information disclosure in data exchange
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for High-Accuracy Privacy-Preserving Mining
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
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
Checking for k-anonymity violation by views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Privacy in database publishing
ICDT'05 Proceedings of the 10th international conference on Database Theory
Authorization views and conditional query containment
ICDT'05 Proceedings of the 10th international conference on Database Theory
Toward privacy in public databases
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Publishing naive Bayesian classifiers: privacy without accuracy loss
Proceedings of the VLDB Endowment
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A privacy violation occurs when the association between an individual identity and data considered private by that individual is obtained by an unauthorized party. Uncertainty and indistinguishability are two independent aspects that characterize the degree of this association being revealed. Indistinguishability refers to the property that the attacker cannot see the difference among a group of individuals, while uncertainty refers to the property that the attacker cannot tell which private value, among a group of values, an individual actually has. This paper investigates the notion of indistinguishability as a general form of anonymity, applicable, for example, not only to generalized private tables, but to relational views and to sets of views obtained by multiple queries over a private database table. It is shown how indistinguishability is highly influenced by certain symmetries among individuals, in the released data, with respect to their private values. The paper provides both theoretical results and practical algorithms for checking if a specific set of views over a private table provide sufficient indistinguishability.