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
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-enhancing k-anonymization of customer data
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
To do or not to do: the dilemma of disclosing anonymized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ICDT'05 Proceedings of the 10th international conference on Database Theory
ARUBA: A Risk-Utility-Based Algorithm for Data Disclosure
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
A utility-theoretic approach to privacy and personalization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Finding "hidden" connections on linkedIn an argument for more pragmatic social network privacy
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk
Transactions on Data Privacy
Self-disclosure decision making based on intimacy and privacy
Information Sciences: an International Journal
Insured access: an approach to ad-hoc information sharing for virtual organizations
Proceedings of the third ACM conference on Data and application security and privacy
Automated buyer profiling control based on human privacy attitudes
Electronic Commerce Research and Applications
Hi-index | 0.00 |
An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized, before being released, it is still possible for an adversary to reconstruct the original data by using additional information that may be available, for example, from other data sources. To date, however, no comprehensive approach exists to quantify such risks. In this paper we develop a framework, based on statistical decision theory, to assess the relationship between the disclosed data and the resulting privacy risk. We relate our framework with the k-anonymity disclosure method; we make the assumptions behind k-anonymity explicit, quantify them, and extend them in several natural directions.