Biblio

The rise of Big Data is leading to an increasing demand for large-scale data-intensive applications (DIAs), which have to analyse massive amounts of personal data (e.g. customers' location, cars' speed, people heartbeat, etc.), some of which can be sensitive, meaning that its confidentiality has to be protected. In this context, DIA providers are responsible for enforcing privacy policies that account for the privacy preferences of data subjects as well as for general privacy regulations. This is the case, for instance, of data brokers, i.e. companies that continuously collect and analyse data in order to provide useful analytics to their clients. Unfortunately, the enforcement of privacy policies in modern DIAs tends to become cumbersome because (i) the number of policies can easily explode, depending on the number of data subjects, (ii) policy enforcement has to autonomously adapt to the application context, thus, requiring some non-trivial runtime reasoning, and (iii) designing and developing modern DIAs is complex per se. For the above reasons, we need specific design and runtime methods enabling so called privacy-by-design in a Big Data context. In this article we propose an approach for specifying, enforcing and checking privacy policies on DIAs designed according to the Google Dataflow model and we show that the enforcement approach behaves correctly in the considered cases and introduces a performance overhead that is acceptable given the requirements of a typical DIA.

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.