Security and inference in multilevel database and knowledge-base systems
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Tuple-level vs. element-level classification
Results of the Sixth Working Conference of IFIP Working Group 11.3 on Database Security on Database security, VI : status and prospects: status and prospects
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IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
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Proceedings of the IFIP TC11 WG 11.3 Twelfth International Working Conference on Database Security XII: Status and Prospects
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Data Level Inference Detection in Database Systems
CSFW '98 Proceedings of the 11th IEEE workshop on Computer Security Foundations
Inference aggregation detection in database management systems
SP'88 Proceedings of the 1988 IEEE conference on Security and privacy
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Existing algorithms on inference detection for database systems mainly employ functional dependencies in the database schema to detect inference, but what they can detect is limited. This paper presents a new data level inference detection algorithm. It can determine whether sensitive information can be disclosed from the user's query history through finding the related tuples between the return results of different queries. If two tuples are related to each other, then they will be merged into one tuple, thus the query history can be compressed. Moreover, the merged tuple has more information than the original two or more tuples. The experiment results show that, as the query number increases, our algorithm can infer almost the whole original relation; meanwhile the query history is compressed remarkablely. The system administrator should restrict user's query number and category to ensure that the database is secure.