C4.5: programs for machine learning
C4.5: programs for machine learning
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
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
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
(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
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Workload-aware anonymization techniques for large-scale datasets
ACM Transactions on Database Systems (TODS)
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
IEEE Transactions on Knowledge and Data Engineering
Universally utility-maximizing privacy mechanisms
Proceedings of the forty-first annual ACM symposium on Theory of computing
On the complexity of differentially private data release: efficient algorithms and hardness results
Proceedings of the forty-first annual ACM symposium on Theory of computing
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
On the geometry of differential privacy
Proceedings of the forty-second ACM symposium on Theory of computing
Interactive privacy via the median mechanism
Proceedings of the forty-second ACM symposium on Theory of computing
Universally optimal privacy mechanisms for minimax agents
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Non-homogeneous generalization in privacy preserving data publishing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Differentially private data release through multidimensional partitioning
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Can the Utility of Anonymized Data be Used for Privacy Breaches?
ACM Transactions on Knowledge Discovery from Data (TKDD)
Differential Privacy via Wavelet Transforms
IEEE Transactions on Knowledge and Data Engineering
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
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Data publishing is an easy and economic means for data sharing, but the privacy risk is a major concern in data publishing. Privacy preservation is a major task in data sharing for organizations like bureau of statistics, and hospitals. While a large number of data publishing models and methods have been proposed, their utility is of concern when a high privacy requirement is imposed. In this paper, we propose a new framework for privacy preserving data publishing. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The semantic meaning is that when an adversary sees a record in a published data set, s/he will have a lower confidence that the record belongs to a victim than not. We design a method integrating sampling and generalization to implement the model. We compare the method with some state-of-the-art methods on privacy-preserving data publishing experimentally, our proposed method provides sound semantic protection of individuals in data and, provides higher data utility.