Views for Multilevel Database Security
IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Reasoning about knowledge
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
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
Concrete Mathematics: A Foundation for Computer Science
Concrete Mathematics: A Foundation for Computer Science
Security of Statistical Databases - Compromise through Attribute Correlational Modeling
Proceedings of the Second International Conference on Data Engineering
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
A Logical Model for Privacy Protection
ISC '01 Proceedings of the 4th International Conference on Information Security
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Inference aggregation detection in database management systems
SP'88 Proceedings of the 1988 IEEE conference on Security and privacy
Controlling logical inference in multilevel database systems
SP'88 Proceedings of the 1988 IEEE conference on Security and privacy
An epistemic framework for privacy protection in database linking
Data & Knowledge Engineering
A computational model to protect patient data from location-based re-identification
Artificial Intelligence in Medicine
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
| Hi-index | 0.00 |
Based on granular computing methodology, we propose two criteria to quantitatively measure privacy invasion. The total cost criterion measures the effort needed for a data recipient to find private information. The average benefit criterion measures the benefit a data recipient obtains when he received the released data. These two criteria remedy the inadequacy of the deterministic privacy formulation proposed in Proceedings of Asia Pacific Medical Informatics Conference, 2000; Int J Med Inform 2003;71:17-23. Granular computing methodology provides a unified framework for these quantitative measurements and previous bin size and logical approaches. These two new criteria are implemented in a prototype system Cellsecu 2.0. Preliminary system performance evaluation is conducted and reviewed.