Secure query-processing strategies

Keefe, T.F.; Thuraisingham, Meena; Tsai, Wei Tek

doi:10.1109/2.16229

Cited by 42 publications

(13 citation statements)

References 8 publications

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“…This is a well-known problem in group communication that much work has gone into remedying (see, for example, [10]) yet it is a desirable property of an inference control scheme (what we call crowd control). As mentioned in Section 1, an alternative approach to inference control that can be viewed as fitting in our general framework is to maintain a bit for each object indicating whether or not it has been released as the result of a query (see, for example, [9]). Our approach allows for more accurate access control because we can distinguish between n users each accessing m − 1 objects in an inference channel of length m, and n(m−1) users each accessing a single object in the channel.…”

Section: Related Workmentioning

confidence: 99%

“…When a user makes a query it is checked against the user's query history and all known inference channels, before granting access to the results of the query. More sophisticated methods of query-time inference control use inference engines to determine access [9]. However, since query histories can be quite long, this approach can result in slow query processing.…”

Section: Introductionmentioning

confidence: 98%

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Dynamic inference control

Staddon

2003

Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

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An inference problem exists in a multilevel database if knowledge of some objects in the database allows information with a higher security level to be inferred. Many such inferences may be prevented prior to any query processing by raising the security level of some of the objects, however this inevitably impedes information access, as a user with low authorization who queries just one of the objects with raised security must seek clearance even when not in danger of making the inference. More flexible access control is possible when inferences are prevented during query processing, however this practice can result in slow query response times. We demonstrate that access control can be made sufficiently dynamic to ensure easy access to the information users are entitled to, while retaining fast query processing. Our inference control schemes provide collusion resistance and have a query processing time that depends only on the length of the inference channels (not on the length of user query histories). In addition, our schemes provide a property we call crowd control that goes beyond collusion resistance to ensure that if a large number of users have queried all but one of the objects in an inference channel, then no one will be able to query the remaining object regardless of the level of collusion resistance provided by the scheme.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Dynamic inference control

Staddon

2003

Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

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“…Detecting and preventing the disclosure of sensitive data via inference channels is referred to as the inference problem [9]. Solutions to the inference problem can be categorized as either a database design [2,3,7,8,11,14,15,17,18,21] or a query processing [4,10,12,13,16,19] solution.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Disclosure Monitor (D 2 Mon): An Improved Query Processing Solution

Toland

Farkas

Eastman

2005

Lecture Notes in Computer Science

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Abstract. The Dynamic Disclosure Monitor (D2 Mon) is a security mechanism that executes during query processing time to prevent sensitive data from being inferred. A limitation of D 2 Mon is that it unnecessarily examines the entire history database in computing inferences. In this paper, we present a process that can be used to reduce the number of tuples that must be examined in computing inferences during query processing time. In particular, we show how a priori knowledge of a database dependency can be used to reduce the search space of a relation when applying database dependencies. Using the database dependencies, we develop a process that forms an index table into the database that identifies those tuples that can be used in satisfying database dependencies. We show how this process can be used to extend D 2 Mon to reduce the number of tuples that must be examined in the history database when computing inferences. We further show that inferences that are computed by D 2 Mon using our extension are sound and complete.

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“…Tools and techniques for ensuring inference control without user privacy can be found in [43,32,47].…”

Section: Related Workmentioning

confidence: 99%

Private inference control

Woodruff

Staddon

2004

Proceedings of the 11th ACM Conference on Computer and Communications Security

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Access control can be used to ensure that database queries pertaining to sensitive information are not answered. This is not enough to prevent users from learning sensitive information though, because users can combine non-sensitive information to discover something sensitive. Inference control prevents users from obtaining sensitive information via such "inference channels", however, existing inference control techniques are not private -that is, they require the server to learn what queries the user is making in order to deny inference-enabling queries.We propose a new primitive -private inference control (PIC) -which is a means for the server to provide inference control without learning what information is being retrieved. PIC is a generalization of private information retrieval (PIR) and symmetrically-private information retrieval (SPIR). While it is straightforward to implement access control using PIR (simply omit sensitive information from the database), it is nontrivial to implement inference control efficiently. We measure the efficiency of a PIC protocol in terms of its communication complexity, its round complexity, and the work the server performs per query. Under existing cryptographic assumptions, we give a PIC scheme which is simultaneously optimal, up to logarithmic factors, in the work the server performs per query, the total communication complexity, and the number of rounds of interaction. We also present a scheme requiring more communication but sufficient storage of state by the server to facilitate private user revocation. Finally, we present a generic reduction which shows that one can focus on designing PIC schemes for which the inference channels take a particularly simple threshold form.

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Secure query-processing strategies

Cited by 42 publications

References 8 publications

Dynamic inference control

Dynamic inference control

Dynamic Disclosure Monitor (D 2 Mon): An Improved Query Processing Solution

Private inference control

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