How Anonymous Is k-Anonymous? Look at Your Quasi-ID
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Non-homogeneous generalization in privacy preserving data publishing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
ACM Transactions on Database Systems (TODS)
Finding all maximally-matchable edges in a bipartite graph
Theoretical Computer Science
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
Secure distributed computation of anonymized views of shared databases
ACM Transactions on Database Systems (TODS)
A practical approximation algorithm for optimal k-anonymity
Data Mining and Knowledge Discovery
k-Concealment: An Alternative Model of k-Type Anonymity
Transactions on Data Privacy
n-confusion: a generalization of k-anonymity
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Anonymizing set-valued data by nonreciprocal recoding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
An Enhanced Utility-Driven Data Anonymization Method
Transactions on Data Privacy
Fast clustering-based anonymization approaches with time constraints for data streams
Knowledge-Based Systems
Improving accuracy of classification models induced from anonymized datasets
Information Sciences: an International Journal
Journal of Computer Security
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In this paper we introduce new notions of k-type anonymizations. Those notions achieve similar privacy goals as those aimed by Sweenie and Samarati when proposing the concept of k-anonymization: an adversary who knows the public data of an individual cannot link that individual to less than k records in the anonymized table. Every anonymized table that satisfies k-anonymity complies also with the anonymity constraints dictated by the new notions, but the converse is not necessarily true. Thus, those new notions allow generalized tables that may offer higher utility than k-anonymized tables, while still preserving the required privacy constraints. We discuss and compare the new anonymization concepts, which we call (1, k )-, (k, k)- and global (1, k)-anonymizations, according to several utility measures. We propose a collection of agglomerative algorithms for the problem of finding such anonymizations with high utility, and demonstrate the usefulness of our definitions and our algorithms through extensive experimental evaluation on real and synthetic datasets.