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
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
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
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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
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
The new Casper: query processing for location services without compromising privacy
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
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Information disclosure under realistic assumptions: privacy versus optimality
Proceedings of the 14th ACM conference on Computer and communications security
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
K-anonymization as spatial indexing: toward scalable and incremental anonymization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Privacy-MaxEnt: integrating background knowledge in privacy quantification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On Anti-Corruption Privacy Preserving Publication
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Injector: Mining Background Knowledge for Data Anonymization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Modeling and Integrating Background Knowledge in Data Anonymization
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Data publishing against realistic adversaries
Proceedings of the VLDB Endowment
Distribution based microdata anonymization
Proceedings of the VLDB Endowment
Algorithm-safe privacy-preserving data publishing
Proceedings of the 13th International Conference on Extending Database Technology
k-jump strategy for preserving privacy in micro-data disclosure
Proceedings of the 13th International Conference on Database Theory
Versatile publishing for privacy preservation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Minimizing minimality and maximizing utility: analyzing method-based attacks on anonymized data
Proceedings of the VLDB Endowment
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
ICDT'05 Proceedings of the 10th international conference on Database Theory
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Numerous privacy-preserving data publishing algorithms were proposed to achieve privacy guarantees such as @?@?diversity. Many of them, however, were recently found to be vulnerable to algorithm-based disclosure-i.e., privacy leakage incurred by an adversary who is aware of the privacy-preserving algorithm being used. This paper describes generic techniques for correcting the design of existing privacy-preserving data publishing algorithms to eliminate algorithm-based disclosure. We first show that algorithm-based disclosure is more prevalent and serious than previously studied. Then, we strictly define Algorithm-SAfe Publishing (ASAP) to capture and eliminate threats from algorithm-based disclosure. To correct the problems of existing data publishing algorithms, we propose two generic tools to be integrated in their design: global look-ahead and local look-ahead. To enhance data utility, we propose another generic tool called stratified pick-up. We demonstrate the effectiveness of our tools by applying them to several popular @?@?diversity algorithms: Mondrian, Hilb, and MASK. We conduct extensive experiments to demonstrate the effectiveness of our tools in terms of data utility and efficiency.