Efficient Enforcement of Action-aware Purpose-based Access Control within Relational Database Management Systems

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

Efficient enforcement of action-aware purpose-based access control within relational database management systems - Extended Abstract

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

Enhancing data privacy and security in Internet of Things through decentralized models and services

exploits a Byzantine Fault Tolerant (BFT) blockchain, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices’ network traffic flows as blockchain transactions. Secondly, we take the challenge to decentralize IoT, and design a hybrid blockchain architecture for IoT, by proposing Hybrid-IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Connection among the PoW sub-blockchains employs a BFT inter-connector framework. We focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds