Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and 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
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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure
Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
L-Cover: Preserving Diversity by Anonymity
SDM '09 Proceedings of the 6th VLDB Workshop on Secure Data Management
On non-cooperative location privacy: a game-theoretic analysis
Proceedings of the 16th ACM conference on Computer and communications security
Transparent anonymization: Thwarting adversaries who know the algorithm
ACM Transactions on Database Systems (TODS)
Algorithm-safe privacy-preserving data publishing
Proceedings of the 13th International Conference on Extending Database Technology
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
k-jump strategy for preserving privacy in micro-data disclosure
Proceedings of the 13th International Conference on Database Theory
A privacy preservation model for facebook-style social network systems
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
Privacy-Preserving Data Publishing: An Overview
Privacy-Preserving Data Publishing: An Overview
Side-Channel Leaks in Web Applications: A Reality Today, a Challenge Tomorrow
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Speaker recognition in encrypted voice streams
ESORICS'10 Proceedings of the 15th European conference on Research in computer security
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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In releasing data with sensitive information, a data owner usually has seemingly conflicting goals, including privacy preservation, utility optimization, and algorithm efficiency. In this paper, we observe that a high computational complexity is usually incurred when an algorithm conflates the processes of privacy preservation and utility optimization. We then propose a novel privacy streamliner approach to decouple those two processes for improving algorithm efficiency. More specifically, we first identify a set of potential privacy-preserving solutions satisfying that an adversary's knowledge about this set itself will not help him/her to violate the privacy property; we can then optimize utility within this set without worrying about privacy breaches since such an optimization is now simulatable by adversaries. To make our approach more concrete, we study it in the context of micro-data release with publicly known generalization algorithms. The analysis and experiments both confirm our algorithms to be more efficient than existing solutions.