1,721,015 research outputs found
A SysML-based approach to requirements analysis and specification of real-time systems.
Full text linkModel-based development is particularly promising in the area of real-time and embedded systems, since it potentially increases the level of automatism and decreases the possible defects, improving the e_ciency of the process and the quality of the product.
Model based approaches are e_ectively supported by notations such as SysML, a modeling language for Systems Engineering that has been recently adopted by the Object Management Group (OMG). SysML is of industrial origin, and it is likely that it will be widely adopted in industry for the development of real-time and embedded systems. Potential obstacles to the adoption of the language on a large scale are the lack of a methodology that drives the modeling activities and the full support for the de_nition of temporal aspects.
The main goal of this PhD work concerns the de_nition of model-based methodological guidelines to the usage of SysML for the analysis and specification of requirements and the early modeling of real-time systems
Enforcement of purpose based access control within relational database management systems
No full textPrivacy 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
Efficient Enforcement of Fine-grained Access Control in Sparkplug-based Industrial Internet of Things
No full textA Framework for Privacy aware Data Management in Relational Databases
No full textThis paper is about MAPaS - modelling and analysis of privacy-aware systems - framework, which targets the development of privacy aware SQL queries operating on a given database. MAPaS supports the specification of purpose and role-based access control policies that regulate the access to data based on purpose compliance, role and purpose-based authorisations. The current version of MAPaS allows the definition of the scheme of the database whose data must be protected and the SQL queries that should be executed on such a database. A rich analysis toolkit allows user to assess the compliance of these queries with the specified privacy policies. The analysis can be done even before the database is populated. The use of MAPaS bring users to define SQL queries which are privacy aware by design
Enhancing MongoDB with Purpose based Access Control
No full textPrivacy has become a key requirement for data management systems. Nevertheless, NoSQL datastores, namely highly scalable non relational database management systems, which often support data management of Internet scale applications, still do not provide support for privacy policies enforcement. With this work, we begin to address this issue, by proposing an approach for the integration of purpose-based policy enforcement capabilities into MongoDB, one of the most popular NoSQL datastore. Our contribution consists of the enhancement of the MongoDB role based access control model with privacy concepts and related enforcement monitor. The proposed monitor is easily integrable into any MongoDB deployment through simple configurations. Experimental results show that our monitor enforces purpose-based access control with low overhead
Access Control Integration in Sparkplug-Based Industrial Internet of Things Systems: Requirements and Open Challenges
No full textPrivacy aware access control for Big Data: a research roadmap
No full textBig Data is an emerging phenomenon that is rapidly changing business models and work styles [1]. Big Data platforms allow the storage and analysis of high volumes of data with heterogeneous format from different sources. This integrated analysis allows the derivation of properties and correlations among data that can then be used for a variety of purposes, such as making predictions that can profitably affect decision processes. As a matter of fact, nowadays Big Data analytics are generally considered an asset for making business decisions. Big Data platforms have been specifically designed to support advanced form of analytics satisfying strict performance and scalability requirements. However, no proper consideration has been devoted so far to data protection. Indeed, although the analyzed data often include personal and sensitive information, with relevant threats to privacy implied by the analysis, so far Big Data platforms integrate quite basic form of access control, and no support for privacy policies. Although the potential benefits of data analysis are manifold, the lack of proper data protection mechanisms may prevent the adoption of Big Data analytics by several companies. This motivates the fundamental need to integrate privacy and security awareness into Big Data platforms. In this paper, we do a first step to achieve this ambitious goal, discussing research issues related to the definition of a framework that supports the integration of privacy aware access control features into existing Big Data platforms
Complementing MongoDB with Advanced Access Control Features: Concepts and Research Challenges
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