Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
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
Secure statistical databases with random sample queries
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
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
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Controlling FD and MVD Inferences in Multilevel Relational Database Systems
IEEE Transactions on Knowledge and Data Engineering
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Privacy-Oriented Data Mining by Proof Checking
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
An architecture for privacy-preserving mining of client information
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
A computational model to protect patient data from location-based re-identification
Artificial Intelligence in Medicine
k-Unlinkability: A privacy protection model for distributed data
Data & Knowledge Engineering
A de-identifier for medical discharge summaries
Artificial Intelligence in Medicine
Secure construction of k-unlinkable patient records from distributed providers
Artificial Intelligence in Medicine
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
k-Concealment: An Alternative Model of k-Type Anonymity
Transactions on Data Privacy
On the inapproximability of maximum intersection problems
Information Processing Letters
A stab at approximating minimum subadditive join
WADS'07 Proceedings of the 10th international conference on Algorithms and Data Structures
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The problem of disseminating a data set for machine learning while controlling the disclosure of data source identity is described using a commuting diagram of functions. This formalization is used to present and analyze an optimization problem balancing privacy and data utility requirements. The analysis points to the application of a generalization mechanism for maintaining privacy in view of machine learning needs. We present new proofs of NP-hardness of the problem of minimizing information loss while satisfying a set of privacy requirements, both with and without the addition of a particular uniform coding requirement. As an initial analysis of the approximation properties of the problem, we show that the cell suppression problem with a constant number of attributes can be approximated within a constant. As a side effect, proofs of NP-hardness of the minimum k{\hbox{-}}{\rm{union}}, maximum k{\hbox{-}}{\rm{intersection}}, and parallel versions of these are presented. Bounded versions of these problems are also shown to be approximable within a constant.