Extending Relational Database Systems to Automatically Enforce Privacy Policies

Agrawal, Rakesh; Bird, Paul; Grandison, Tyrone; Kiernan, Jerry; Logan, S.; Rjaibi, Walid

doi:10.1109/icde.2005.64

Cited by 90 publications

(68 citation statements)

References 3 publications

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“…The models in [1,18] support only row-level constraints, those in [7,12] support only column-level constraints, and the model in [5] supports both. Our security policy model subsumes all these approaches as well as approaches where the security policy is expressed using views.…”

Section: Security Policy Modelmentioning

confidence: 99%

“…There is, however, no common terminology to refer to the problem of computing answers to queries in the presence of access control policies. For example, "secure querying" [9], "enforcing data confidentiality" [15], and "Fine-Grained Access Control" [5,16,19] have all been used in this context. In the following, we refer to this problem as Security-Aware Query Processing (SAQP).…”

Section: Introductionmentioning

confidence: 99%

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Optimal security-aware query processing

Guarnieri

Basin

2014

Proc. VLDB Endow.

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Security-Aware Query Processing is the problem of computing answers to queries in the presence of access control policies. We present general impossibility results for the existence of optimal algorithms for Security-Aware Query Processing and classify query languages for which such algorithms exist. In particular, we show that for the relational calculus there are no optimal algorithms, whereas optimal algorithms exist for some of its fragments, such as the existential fragment.We also establish relationships between two different models of Fine-Grained Access Control, called Truman and NonTruman models, which have been previously presented in the literature as distinct. For optimal Security-Aware Query Processing, we show that the Non-Truman model is a special case of the Truman model for boolean queries in the relational calculus, moreover the two models coincide for more powerful languages, such as the relational calculus with aggregation operators. In contrast, these two models are distinct for non-boolean queries.

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Section: Security Policy Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal security-aware query processing

Guarnieri

Basin

2014

Proc. VLDB Endow.

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“…The structure of this paper 1 is as follows: section II recalls some basics on Abstract Interpretation theory and on the concrete and abstract semantics of database query languages. We describe the proposed OFGAC 1 The paper is a revised and extended version of [10,12] framework to the context of RDBMS and XML documents in sections III and IV respectively. In section V, we discuss the related works in the literature.…”

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

confidence: 99%

“…They presented a formal model of secret information disclosure that defines a query to be suspicious if and only if the disclosed secret could be inferred from its answer. Agrawal et al [1] introduced the syntax of a fine grained restriction command at column level, row level, or cell level. The enforcement algorithm automatically combines the restrictions relevant to individual queries annotated with purpose and recipient information, and transforms the users' queries into equivalent queries over a dynamic view that implements the restriction.…”

Section: Related Workmentioning

confidence: 99%

Observation-Based Fine Grained Access Control for XML Documents

Halder

Cortesi

2011

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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“…Subsequent works have proposed solutions for implementing HDBs. In [1], authors address the problem of how current relational DBMS can be transformed into their privacy-preserving equivalents. From specifications of privacy policies, they propose an algorithm that defines restrictions (on columns, rows, and cells) to limit data access.…”

Section: Hippocratic Databasesmentioning

confidence: 99%

Supporting Data Privacy in P2P Systems

Jawad

Serrano-Alvarado

Valduriez

2013

Lecture Notes in Social Networks

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Abstract. Peer-to-Peer (P2P) systems have been very successful for large-scale data sharing. However, sharing sensitive data, like in online social networks, without appropriate access control, can have undesirable impact on data privacy. Data can be accessed by everyone (by potentially untrusted peers) and used for everything (e.g., for marketing or activities against the owner's preferences or ethics). Hippocratic databases (HDB) provide an effective solution to this problem, by integrating purposebased access control for privacy protection. However, the use of HDB has been restricted to centralized systems. This chapter gives an overview of current solutions for supporting data privacy in P2P systems, and develops in more details a complete solution based on HDB.Keywords: data privacy, P2P systems, DHT, Hippocratic databases, purposebased access control, trust. IntroductionData privacy is the right of individuals to determine for themselves when, how, and to what extent information about them is communicated to others [40]. It has been treated by many organizations and legislations that have defined well accepted principles. According to OECD 3 , data privacy should consider: collection limitation, purpose specification, use limitation, data quality, security safeguards, openness, individual participation, and accountability. From these principles, we underline purpose specification which states that data owners should be able to specify the data access purposes for which their data will be collected, stored, and used.With the advent of Online Social Networks (OLSN), data privacy has become a major concern. An OLSN is formed by people having something in common and connected by social relationships, such as friendship, hobbies, or coworking, in order to exchange information [11]. Many communities use OLSNs to share data in both professional and non-professional environments. Examples of professional OLSNs are Shanoir 4 , designed for the neuroscience community to 3 Organization for Economic Co-operation and Development. One of the world's largest and most reliable source of comparable statistics on economic and social data (http://www.oecd.org/). 4 www.shanoir.org/ 3 archive, share, search, and visualize neuroimaging data, or medscape 5 , designed for the medical community to share medical experience and medical data. There are also non-professional OLSNs for average citizens and amateurs in different domains such as Carenity 6 , designed for patients and their relatives to share medical information about them in order to help medical research. Another example is DIYbio 7 , dedicated to make biology accessible for citizen scientists, amateur biologists, and biological engineers, who share research results. The most popular OLSN, Facebook, with hundreds millions of users, enables groups of friends to share all kinds of personal information among themselves.Scalable data sharing among community members is critical for an OLSN system. Two main solutions have emerged for scalable data sharing: cloud computing a...

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Extending Relational Database Systems to Automatically Enforce Privacy Policies

Cited by 90 publications

References 3 publications

Optimal security-aware query processing

Optimal security-aware query processing

Observation-Based Fine Grained Access Control for XML Documents

Supporting Data Privacy in P2P Systems

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