Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
A formal analysis of information disclosure in data exchange
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Injecting utility into anonymized datasets
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Approximate algorithms for K-anonymity
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
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility of Knowledge Extracted from Unsanitized Data when Applied to Sanitized Data
PST '08 Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust
An Attack on the Privacy of Sanitized Data that Fuses the Outputs of Multiple Data Miners
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
An information theoretic privacy and utility measure for data sanitization mechanisms
Proceedings of the second ACM conference on Data and Application Security and Privacy
Privacy consensus in anonymization systems via game theory
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
| Hi-index | 0.00 |
Published data is prone to privacy attacks. Sanitization methods aim to prevent these attacks while maintaining usefulness of the data for legitimate users. Quantifying the trade-off between usefulness and privacy of published data has been the subject of much research in recent years. We propose a pragmatic framework for evaluating sanitization systems in real-life and use data mining utility as a universal measure of usefulness and privacy. We propose a definition for data mining utility that can be tuned to capture the needs of data users and the adversaries' intentions in a setting that is specified by a database, a candidate sanitization method, and privacy and utility concerns of data owner. We use this framework to evaluate and compare privacy and utility offered by two well-known sanitization methods, namely k-anonymity and ε-differential privacy, when UCI's "Adult" dataset and the Weka data mining package is used, and utility and privacy measures are defined for users and adversaries. In the case of k-anonymity, we compare our results with the recent work of Brickell and Shmatikov (KDD 2008), and show that using data mining algorithms increases their proposed adversarial gains.