1986 IEEE Symposium on Security and Privacy 1986
DOI: 10.1109/sp.1986.10012
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Views for Multilevel Database Security

Abstract: Because views on relational database systems mathematically define arbitrary sets of stored and derived data, they have been proposed as a way of handling context-and contenbdependent classification, dynamic classification, inference, aggregation, and sanitization in multilevel database systems. This paper describes basic view concepts for a multilevelsecure relational database model that addresses the above issues. The model treats stored and derived data uniformly within the database schema. All data in the … Show more

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Cited by 45 publications
(21 citation statements)
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References 7 publications
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“…There are many ways to avoid storing too much data in practice. Work on multi-level databases [21,30] suggests it is both useful and practically feasible to store multiple versions of data corresponding to different access levels. The question becomes, then, how to avoid storing too much data due to too id name location jid jvars 1 "Carol's ... party" "Schloss Dagstuhl" 1 "x=True" 2 "Private event"…”
Section: Data Representation Considerationsmentioning
confidence: 99%
“…There are many ways to avoid storing too much data in practice. Work on multi-level databases [21,30] suggests it is both useful and practically feasible to store multiple versions of data corresponding to different access levels. The question becomes, then, how to avoid storing too much data due to too id name location jid jvars 1 "Carol's ... party" "Schloss Dagstuhl" 1 "x=True" 2 "Private event"…”
Section: Data Representation Considerationsmentioning
confidence: 99%
“…They allow users to manage data presentation without affecting underlying data. Views are also used to enhance database security [58,59]. We exploit and enhance views so as to provide users with lightweight methods for adjusting presentation of personal data and managing information flows.…”
Section: Boundaries As Two-way Permeable Views In Contextmentioning
confidence: 99%
“…Important:{1, 3,5,6,8,19,[23][24][25][26][27][28]32,33,35,36,[38][39][40][41]7,9,11,14,17,20,22,29,30,34,37> Unimportant: (4,12,13,15,16,18,19,21,3) Unimportant: (7,8,9,11,13,22,27,28,30,31,35,36,37, 40,41) Table 4. SVM detection efficacy for dos using different features obtained from svdf ranking…”
Section: Ranking the Significance Of Inputsmentioning
confidence: 99%
“…IDSs are classified, based on their functionality, as misuse detectors and anomaly detectors. Misuse detection systems use the attack of well-know patterns as the basis for detection [3,4,5,6,7]. Anomaly detection systems use user profiles as the basis for detection; any deviation from the normal user behavior is termed as intrusions [3,4,5,8,9,10].…”
Section: Introductionmentioning
confidence: 99%
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