The NP-completeness column: An ongoing guide
Journal of Algorithms
A faster strongly polynomial minimum cost flow algorithm
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
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
Communications of the ACM
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
(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
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering
Data and Structural k-Anonymity in Social Networks
Privacy, Security, and Trust in KDD
Towards Fully Multivariate Algorithmics: Some New Results and Directions in Parameter Ecology
Combinatorial Algorithms
The hardness and approximation algorithms for l-diversity
Proceedings of the 13th International Conference on Extending Database Technology
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Achieving anonymity via clustering
ACM Transactions on Algorithms (TALG)
Movement Data Anonymity through Generalization
Transactions on Data Privacy
A firm foundation for private data analysis
Communications of the ACM
Resolving the complexity of some data privacy problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Providing K-Anonymity in location based services
ACM SIGKDD Explorations Newsletter
Deconstructing intractability-A multivariate complexity analysis of interval constrained coloring
Journal of Discrete Algorithms
Anonymizing binary and small tables is hard to approximate
Journal of Combinatorial Optimization
Knowledge and Information Systems
Pattern-guided data anonymization and clustering
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
ICDT'05 Proceedings of the 10th international conference on Database Theory
User k-anonymity for privacy preserving data mining of query logs
Information Processing and Management: an International Journal
A practical approximation algorithm for optimal k-anonymity
Data Mining and Knowledge Discovery
Parameterized Complexity
Parameterized complexity of k-anonymity: hardness and tractability
Journal of Combinatorial Optimization
The l-Diversity problem: Tractability and approximability
Theoretical Computer Science
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A matrix M is said to be k-anonymous if for each row r in M there are at least k 驴 1 other rows in M which are identical to r. The NP-hard k-Anonymity problem asks, given an n 脳 m-matrix M over a fixed alphabet and an integer s 0, whether M can be made k-anonymous by suppressing (blanking out) at most s entries. Complementing previous work, we introduce two new "data-driven" parameterizations for k-Anonymity--the number t in of different input rows and the number t out of different output rows--both modeling aspects of data homogeneity. We show that k-Anonymity is fixed-parameter tractable for the parameter t in , and that it is NP-hard even for t out = 2 and alphabet size four. Notably, our fixed-parameter tractability result implies that k-Anonymity can be solved in linear time when t in is a constant. Our computational hardness results also extend to the related privacy problems p-Sensitivity and ℓ-Diversity, while our fixed-parameter tractability results extend to p-Sensitivity and the usage of domain generalization hierarchies, where the entries are replaced by more general data instead of being completely suppressed.