Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Mining Classification Rules from Datasets with Large Number of Many-Valued Attributes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third 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
Generalized multidimensional data mapping and query processing
ACM Transactions on Database Systems (TODS)
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(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
Utility-based anonymization using local recoding
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
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Data privacy protection in multi-party clustering
Data & Knowledge Engineering
Privately detecting bursts in streaming, distributed time series data
Data & Knowledge Engineering
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extending l-Diversity for Better Data Anonymization
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Achieving P-Sensitive K-Anonymity via Anatomy
ICEBE '09 Proceedings of the 2009 IEEE International Conference on e-Business Engineering
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Privacy-preserving data mining through knowledge model sharing
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Towards a reference service model for the Web of Services
Data & Knowledge Engineering
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Information Sciences: an International Journal
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Generalization is an important technique for protecting privacy in data dissemination. In the framework of generalization, @?-diversity is a strong notion of privacy. However, since existing @?-diversity measures are defined in terms of the most specific (rather than general) sensitive attribute (SA) values, algorithms based on these measures can have narrow eligible ranges for data that has a heavily skewed distribution of SA values and produce anonymous data that has a low utility. In this paper, we propose a new @?-diversity measure called the functional (@t, @?)-diversity, which extends @?-diversity by using a simple function to constrain frequencies of base SA values that are induced by general SA values. As a result, algorithms based on (@t, @?)-diversity may generalize SA values, thus are much less constrained by skew SA distributions. We show that (@t, @?)-diversity is more flexible and elaborate than existing @?-diversity measures. We present an efficient heuristic algorithm that uses a novel order of quasi-identifier (QI) values to achieve (@t, @?)-diversity. We compare our algorithm with two state-of-the-art algorithms that are based on existing @?-diversity measures. Our preliminary experimental results indicate that our algorithm not only provides a stronger privacy protection but also results in better utility of anonymous data.