Confidentiality Policies for Controlled Query Evaluation

Biskup, Joachim; Weibert, Torben

doi:10.1007/978-3-540-73538-0_1

Cited by 11 publications

(9 citation statements)

References 23 publications

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“…We can reduce confidentiality targets to potential secrets, and reuse the enforcement methods from the present paper. Details are given in [7]. Previous work on complete databases also investigated the special case that the user is not aware of the contents of the confidentiality policy ("unknown policies" [1,17]).…”

Section: Conclusion and Further Workmentioning

confidence: 98%

“…-The uniform refusal method relies on meta inferences, calculated by inference meta (7). Thus, also disjunctive inferences have to be considered when determining the security configuration.…”

Section: Combined Lying and Refusalmentioning

confidence: 99%

“…Let cqe refusal be the enforcement method built up of inference meta (7), violates single (12), and a safe refusal censor (Definition 20). The precondition (15) can be expanded as…”

Section: Confidentialitymentioning

confidence: 99%

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Keeping secrets in incomplete databases

Biskup

Weibert

2007

Int. J. Inf. Secur.

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Controlled query evaluation (CQE) preserves confidentiality in information systems at runtime. A confidentiality policy specifies the information a certain user is not allowed to know. At each query, a censor checks whether the answer would enable the user to learn any classified information. In that case, the answer is distorted, either by lying or by refusal. We introduce a framework in which CQE can be analyzed wrt. possibly incomplete logic databases. For each distortion method, lying and refusal, a class of confidentiality-preserving mechanisms is presented. Furthermore, we specify a third approach that combines lying and refusal and compensates the disadvantages of the respective uniform methods. The enforcement methods are compared to the existing methods for complete databases.

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Section: Conclusion and Further Workmentioning

confidence: 98%

Section: Combined Lying and Refusalmentioning

confidence: 99%

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Keeping secrets in incomplete databases

Biskup

Weibert

2007

Int. J. Inf. Secur.

Self Cite

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“…A similar pattern based policy definition is complemented with precise default semantics and access conflict resolution in [15].Postfiltering query results to enforce the access control policy is also possible [6] but this strategy may have performance issues without dedicated support.…”

Section: Related Workmentioning

confidence: 99%

Query-based access control for secure collaborative modeling using bidirectional transformations*

Bergmann

Debreceni

Ráth

et al. 2016

Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems

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Large-scale model-driven system engineering projects are carried out collaboratively. Engineering artifacts stored in model repositories are developed in either offline (checkoutmodify-commit) or online (GoogleDoc-style) scenarios. Complex systems frequently integrate models and components developed by different teams, vendors and suppliers. Thus confidentiality and integrity of design artifacts need to be protected by access control policies.We propose a technique for secure collaborative modeling where (1) fine-grained access control for models can be defined by model queries, and (2) such access control policies are strictly enforced by bidirectional model transformations. Each collaborator obtains a filtered local copy of the model containing only those model elements which they are allowed to read; write access control policies are checked on the server upon submitting model changes. We illustrate the approach and carry out an initial scalability assessment using a case study of the MONDO EU project.

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“…Data privacy and access control in incomplete propositional databases has been studied in [6], [7], [31]. They take a different approach, control query evaluation (CQE), to fine-grained access control.…”

Section: Related Workmentioning

confidence: 99%

Achieving Data Privacy through Secrecy Views and Null-Based Virtual Updates

Bertossi

2013

IEEE Trans. Knowl. Data Eng.

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We may want to keep sensitive information in a relational database hidden from a user or group thereof. We characterize sensitive data as the extensions of secrecy views. The database, before returning the answers to a query posed by a restricted user, is updated to make the secrecy views empty or a single tuple with null values. Then, a query about any of those views returns no meaningful information. Since the database is not supposed to be physically changed for this purpose, the updates are only virtual, and also minimal. Minimality makes sure that query answers, while being privacy preserving, are also maximally informative. The virtual updates are based on null values as used in the SQL standard. We provide the semantics of secrecy views, virtual updates, and secret answers to queries. The different instances resulting from the virtually updates are specified as the models of a logic program with stable model semantics, which becomes the basis for computation of the secret answers.Index Terms-Data privacy, views, query answering, null values, view updates, answer set programs, database repairs.

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Confidentiality Policies for Controlled Query Evaluation

Cited by 11 publications

References 23 publications

Keeping secrets in incomplete databases

Keeping secrets in incomplete databases

Query-based access control for secure collaborative modeling using bidirectional transformations*

Achieving Data Privacy through Secrecy Views and Null-Based Virtual Updates

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