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
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Data Privacy through Optimal k-Anonymization
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
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
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)
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Data & Knowledge Engineering
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering
A framework for efficient data anonymization under privacy and accuracy constraints
ACM Transactions on Database Systems (TODS)
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Generating microdata with p-sensitive k-anonymity property
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
ICDT'05 Proceedings of the 10th international conference on Database Theory
Secure distributed computation of anonymized views of shared databases
ACM Transactions on Database Systems (TODS)
k-Concealment: An Alternative Model of k-Type Anonymity
Transactions on Data Privacy
Improving accuracy of classification models induced from anonymized datasets
Information Sciences: an International Journal
The effect of homogeneity on the computational complexity of combinatorial data anonymization
Data Mining and Knowledge Discovery
Hi-index | 0.00 |
k-Anonymity is a privacy preserving method for limiting disclosure of private information in data mining. The process of anonymizing a database table typically involves generalizing table entries and, consequently, it incurs loss of relevant information. This motivates the search for anonymization algorithms that achieve the required level of anonymization while incurring a minimal loss of information. The problem of k-anonymization with minimal loss of information is NP-hard. We present a practical approximation algorithm that enables solving the k-anonymization problem with an approximation guarantee of O(ln k). That algorithm improves an algorithm due to Aggarwal et al. (Proceedings of the international conference on database theory (ICDT), 2005) that offers an approximation guarantee of O(k), and generalizes that of Park and Shim (SIGMOD '07: proceedings of the 2007 ACM SIGMOD international conference on management of data, 2007) that was limited to the case of generalization by suppression. Our algorithm uses techniques that we introduce herein for mining closed frequent generalized records. Our experiments show that the significance of our algorithm is not limited only to the theory of k-anonymization. The proposed algorithm achieves lower information losses than the leading approximation algorithm, as well as the leading heuristic algorithms. A modified version of our algorithm that issues 驴-diverse k-anonymizations also achieves lower information losses than the corresponding modified versions of the leading algorithms.