The Experts below are selected from a list of 61050 Experts worldwide ranked by ideXlab platform
Elena Ferrari - One of the best experts on this subject based on the ideXlab platform.
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efficient enforcement of action aware purpose based access control within relational Database Management Systems
International Conference on Data Engineering, 2016Co-Authors: Pietro Colombo, Elena FerrariAbstract:Although Database Management Systems (DBMSs) enforce access control according to a variety of models (see [2] for an overview), the majority of them do not integrate native privacy protection mechanisms. This void has been partially filled out with the advent of purpose based access control, as this access control model has brought to the integration of basic privacy preservation functionalities into DBMSs. Even though purposes represent a key feature of privacy policies, DBMSs' privacy awareness can be significantly increased considering additional privacy related aspects. With this work we do a step to achieve this goal by focusing on the actions performed by queries on data and the categories of the accessed data. We propose an access control model that supports highly customized privacyaware access control policies and significantly improves the basic privacy preservation capabilities of the purpose based model. The proposed model is complemented with an efficient enforcement monitor, which can be easily integrated into relational DBMSs. Early experimental evaluations show the efficiency of the proposed framework.
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Efficient Enforcement of Action-Aware Purpose-Based Access Control within Relational Database Management Systems
IEEE Transactions on Knowledge and Data Engineering, 2015Co-Authors: Pietro Colombo, Elena FerrariAbstract:Among the variety of access control models proposed for Database Management Systems (DBMSs) a key role is covered by the purpose-based access control model, which, while enforcing access control, also achieves basic privacy preservation. We believe that DBMSs could greatly take benefit from the integration of an enhanced purpose based model supporting highly customized and efficient access control. Therefore, in this paper, we propose a purpose-based model that supports action-aware policy specification and a related efficient enforcement framework to be integrated into relational DBMSs. The experimental evaluation we have performed shows the feasibility and efficiency of the proposed framework.
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enforcement of purpose based access control within relational Database Management Systems
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Pietro Colombo, Elena FerrariAbstract:Privacy is becoming a key requirement for ICT applications that handle personal data. However, Database Management Systems (DBMSs), which are devoted to data collection and processing by definition, still do not provide the proper support for privacy policies. Policies are enforced by ad-hoc programmed software modules that complement DBMS access control services. This practice is time consuming, error prone, and neither general nor scalable. This work does a first step to overcome these limits. We propose a systematic approach to the automatic development of a monitor that regulates the execution of SQL queries based on purpose based privacy policies. The proposed solution does not require programming, it is general, platform independent and usable with most of the existing relational DBMSs.
Pietro Colombo - One of the best experts on this subject based on the ideXlab platform.
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efficient enforcement of action aware purpose based access control within relational Database Management Systems
International Conference on Data Engineering, 2016Co-Authors: Pietro Colombo, Elena FerrariAbstract:Although Database Management Systems (DBMSs) enforce access control according to a variety of models (see [2] for an overview), the majority of them do not integrate native privacy protection mechanisms. This void has been partially filled out with the advent of purpose based access control, as this access control model has brought to the integration of basic privacy preservation functionalities into DBMSs. Even though purposes represent a key feature of privacy policies, DBMSs' privacy awareness can be significantly increased considering additional privacy related aspects. With this work we do a step to achieve this goal by focusing on the actions performed by queries on data and the categories of the accessed data. We propose an access control model that supports highly customized privacyaware access control policies and significantly improves the basic privacy preservation capabilities of the purpose based model. The proposed model is complemented with an efficient enforcement monitor, which can be easily integrated into relational DBMSs. Early experimental evaluations show the efficiency of the proposed framework.
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Efficient Enforcement of Action-Aware Purpose-Based Access Control within Relational Database Management Systems
IEEE Transactions on Knowledge and Data Engineering, 2015Co-Authors: Pietro Colombo, Elena FerrariAbstract:Among the variety of access control models proposed for Database Management Systems (DBMSs) a key role is covered by the purpose-based access control model, which, while enforcing access control, also achieves basic privacy preservation. We believe that DBMSs could greatly take benefit from the integration of an enhanced purpose based model supporting highly customized and efficient access control. Therefore, in this paper, we propose a purpose-based model that supports action-aware policy specification and a related efficient enforcement framework to be integrated into relational DBMSs. The experimental evaluation we have performed shows the feasibility and efficiency of the proposed framework.
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enforcement of purpose based access control within relational Database Management Systems
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Pietro Colombo, Elena FerrariAbstract:Privacy is becoming a key requirement for ICT applications that handle personal data. However, Database Management Systems (DBMSs), which are devoted to data collection and processing by definition, still do not provide the proper support for privacy policies. Policies are enforced by ad-hoc programmed software modules that complement DBMS access control services. This practice is time consuming, error prone, and neither general nor scalable. This work does a first step to overcome these limits. We propose a systematic approach to the automatic development of a monitor that regulates the execution of SQL queries based on purpose based privacy policies. The proposed solution does not require programming, it is general, platform independent and usable with most of the existing relational DBMSs.
Johannchristoph Freytag - One of the best experts on this subject based on the ideXlab platform.
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sieve a middleware approach to scalable access control for Database Management Systems
arXiv: Databases, 2020Co-Authors: Primal Pappachan, Roberto Yus, Sharad Mehrotra, Johannchristoph FreytagAbstract:Current approaches of enforcing FGAC in Database Management Systems (DBMS) do not scale in scenarios when the number of policies are in the order of thousands. This paper identifies such a use case in the context of emerging smart spaces wherein Systems may be required by legislation, such as Europe's GDPR and California's CCPA, to empower users to specify who may have access to their data and for what purposes. We present Sieve, a layered approach of implementing FGAC in existing Database Systems, that exploits a variety of it's features such as UDFs, index usage hints, query explain; to scale to large number of policies. Given a query, Sieve exploits it's context to filter the policies that need to be checked. Sieve also generates guarded expressions that saves on evaluation cost by grouping the policies and cuts the read cost by exploiting Database indices. Our experimental results, on two DBMS and two different datasets, show that Sieve scales to large data sets and to large policy corpus thus supporting real-time access in applications including emerging smart environments.
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ontology based query processing in Database Management Systems
Lecture Notes in Computer Science, 2003Co-Authors: Chokri Ben Necib, Johannchristoph FreytagAbstract:The use of semantic knowledge in its various forms has become an important aspect in managing data in Database and information Systems. In the form of integrity constraints, it has been used intensively in query optimization for some time. Similarly, data integration techniques have utilized semantic knowledge to handle heterogeneity for query processing on distributed information sources in a graceful manner. Recently, ontologies have become a “buzz word” for the semantic web and semantic data processing. In fact, they play a central role in facilitating the exchange of data between the several sources. In this paper, we present a new approach using ontology knowledge for query processing within a single relational Database to extend the result of a query in a semantically meaningful way. We describe how an ontology can be effectively exploited to rewrite a user query into another query such that the new query provides additional meaningful results that satisfy the intention of the user. We outline a set of query transformation rules and describe by using a semantic Model the necessary criteria to prove their validity.
Bhavani Thuraisingham - One of the best experts on this subject based on the ideXlab platform.
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Security and privacy for multimedia Database Management Systems
Multimedia Tools and Applications, 2007Co-Authors: Bhavani ThuraisinghamAbstract:This paper describes security and privacy issues for multimedia Database Management Systems. Multimedia data includes text, images, audio and video. It describes access control for multimedia Database Management Systems and describes security policies and security architectures for such Systems. Privacy problems that result from multimedia data mining are also discussed.
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Secure interoperability of trusted Database Management Systems
ACM Sigsac Review, 1992Co-Authors: Bhavani ThuraisinghamAbstract:We describe issues on interconnecting trusted Database Management Systems. In particular, issues on handling heterogeneity and autonomy are discussed.
A. R. Yardi - One of the best experts on this subject based on the ideXlab platform.
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Adaptive neuro-fuzzy technique for performance tuning of Database Management Systems
Evolving Systems, 2013Co-Authors: S. F. Rodd, U. P. Kulkarni, A. R. YardiAbstract:A recent trend in Database performance tuning is towards self tuning for some of the important benefits like efficient use of resources, improved performance and low cost of ownership that the auto-tuning offers. Most modern Database Management Systems (DBMS) have introduced several dynamically tunable parameters that enable the implementation of self tuning Systems. An appropriate mix of various tuning parameters results in significant performance enhancement either in terms of response time of the queries or the overall throughput. The choice and extent of tuning of the available tuning parameters must be based on the impact of these parameters on the performance and also on the amount and type of workload the DBMS is subjected to. The tedious task of manual tuning and also non-availability of expert Database administrators (DBAs), it is desirable to have a self tuning Database system that not only relieves the DBA of the tedious task of manual tuning, but it also eliminates the need for an expert DBA. Thus, it reduces the total cost of ownership of the entire software system. A self tuning system also adapts well to the dynamic workload changes and also user loads during peak hours ensuring acceptable application response times. In this paper, a novel technique that combines learning ability of the artificial neural network and the ability of the fuzzy system to deal with imprecise inputs are employed to estimate the extent of tuning required. Furthermore, the estimated values are moderated based on knowledgebase built using experimental findings. The experimental results show significant performance improvement as compared to built in self tuning feature of the DBMS.
