Abstract Interpretation of Recursive Queries

Cortesi, Agostino; Halder, Raju

doi:10.1007/978-3-642-36071-8_12

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…Thus, in case of AVG (Age ), we get a = f n ({35, . Similarly, in case of COUNT ( * ), we get a = count (G yes )= [3,3] and b = count (G ) = [5,5]. Thus, COUNT ( * ) = [3,5].…”

Section: Query Evaluation Under Ofgacmentioning

confidence: 90%

“…Similarly, in case of COUNT ( * ), we get a = count (G yes )= [3,3] and b = count (G ) = [5,5]. Thus, COUNT ( * ) = [3,5]. Observe that the result is sound, i.e., ξ 2 ∈ γ(ξ 2 ) where ξ 2 is the result of a concrete query Q 2 ∈ γ(Q 2 ).…”

Section: Query Evaluation Under Ofgacmentioning

confidence: 91%

“…However, practically this is not feasible in all cases, as the sensitivity level and the access-privilege of the same element might be different for different users, and the integrity of data is compromised. For instance, when an integer data (say, 10) is partially viewed as an interval (say, [5,25]), we can not split it. To cope with this situation, we propose an Observationbased Fine Grained Access Control (OFGAC) mechanism where data are made accessible at various levels of abstractions according to their sensitivity levels, based on the Abstract Interpretation framework.…”

Section: Figure 1: a Document Type Definition (Dtd) And Its Instancementioning

confidence: 99%

“…In this section, we recall some basics on the Abstract Interpretation theory [6,7] and on the concrete and abstract semantics of database query languages [5,11].…”

Section: Preliminariesmentioning

confidence: 99%

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Fine Grained Access Control for Relational Databases by Abstract Interpretation

Halder

Cortesi

2013

Communications in Computer and Information Science

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Abstract:In this paper, we propose an observation-based fine grained access control (OFGAC) mechanism where data are made accessible at various levels of abstractions according to their sensitivity levels. In this setting, unauthorized users are not able to infer the exact content of the confidential data, while they are allowed to get partial information out of it, according to their access rights. The traditional fine grained access control (FGAC) can be seen as a special case of the OFGAC framework.

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“…Thus, in case of AVG (Age ), we get a = f n ({35, . Similarly, in case of COUNT ( * ), we get a = count (G yes )= [3,3] and b = count (G ) = [5,5]. Thus, COUNT ( * ) = [3,5].…”

Section: Query Evaluation Under Ofgacmentioning

confidence: 90%

Section: Query Evaluation Under Ofgacmentioning

confidence: 91%

Section: Figure 1: a Document Type Definition (Dtd) And Its Instancementioning

confidence: 99%

“…In this section, we recall some basics on the Abstract Interpretation theory [6,7] and on the concrete and abstract semantics of database query languages [5,11].…”

Section: Preliminariesmentioning

confidence: 99%

See 2 more Smart Citations

Fine Grained Access Control for Relational Databases by Abstract Interpretation

Halder

Cortesi

2013

Communications in Computer and Information Science

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“…Another paradigm was voted into SQL:1999 based on recursive Common Table Expressions. It has been implemented in numerous database systems [2], while the academia worked on optimization methods for such queries [3,4,5]. However, there are still database systems whose SQL dialect does not include any recursion in queries.…”

Section: Introductionmentioning

confidence: 99%

On Materializing Paths for Faster Recursive Querying

Boniewicz

Wiśniewski

Stencel

2014

Advances in Intelligent Systems and Computing

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Abstract. Recursive data structures are often used in business applications. They store data on e.g. corporate hierarchies, product categories and bill-of-material. Therefore, recursive queries as introduced by SQL:1999 or formerly implemented by Oracle constitute a useful facility for application programmers. Unfortunately, recursive queries are not implemented by a number of database systems with MySQL as the most profound example. If an application has such a database as the backend storage, recursive queries will be usually hard-coded at the client side. This is not efficient. In this paper we propose using redundant data structures to answer recursive queries quicker. Such structures must be synchronized in response to updates of data. This means a significant processing overhead for updates. We present experimental evaluation to show loses and gains caused by our solution for various usage scenarios. They prove the feasibility of our proposal. We show our proof-of-concept implementation as a part of the Hibernate framework. Thus, application programmers are separated from all internals of necessary database objects and triggers. These are created automatically by Hibernate generators.

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