Reasoning about knowledge and probability
Journal of the ACM (JACM)
Reasoning about knowledge
Fundamentals of partial modal logic
Partiality, modality, and nonmonotonicity
Trust and risk in Internet commerce
Trust and risk in Internet commerce
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Graph Theory With Applications
Graph Theory With Applications
On modal and fuzzy decision logics based on rough set theory
Fundamenta Informaticae
Simple Epistemic Logic for Relational Database
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
How Much Privacy? - A System to Safe Guard Personal Privacy while Releasing Databases
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Quantifying Privacy Leakage through Answering Database Queries
ISC '02 Proceedings of the 5th International Conference on Information Security
An epistemic framework for privacy protection in database linking
Data & Knowledge Engineering
Granulation as a privacy protection mechanism
Transactions on rough sets VII
A grc-based approach to social network data protection
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Unauthorized inferences in semistructured databases
Information Sciences: an International Journal
Medical privacy protection based on granular computing
Artificial Intelligence in Medicine
On Modal and Fuzzy Decision Logics Based on Rough Set Theory
Fundamenta Informaticae
A probabilistic hybrid logic for sanitized information systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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In this paper, we present a logical model for privacy protection problem in the database linking context. Assume in the data center, there are a large amount of data records. Each record has some public attributes the values of which are known to the public and some confidential attributes the values of which are to be protected. When a data table is released, the data manager must assure that the receiver would not know the confidential data of any particular individuals by linking the releasing data and the prior information he had before receiving the data.To solve the problem, we propose a simple epistemic logic to model the user's knowledge. In the model, the concept of safety is rigorously defined and an effective approach is given to test the safety of the released data. It is shown that some generalization operations can be applied to the original data to make them less precise and the release of the generalized data may prevent the violation of privacy. Two kinds of generalization operations are considered. The level-based one is more restrictive, however, a bottom-up search method can be used to find the most informative data satisfying the safety requirement. On the other hand, the set-based one is more flexible, however, the computational complexity of searching through the whole spaces of this kinds of operations is much higher than the previous one though graph theory is used to simplify the discussion. As a result, heuristic methods may be needed to improve the efficiency.