Defining, enforcing and checking privacy policies in data-intensive applications

• Published in: 2018 IEEE ACM 13th International Symposium on Software Engineering for Adaptive and Self Managing Systems (SEAMS) pp. 172 - 182
• Main Authors: Guerriero, Michele, Tamburri, Damian Andrew, Di Nitto, Elisabetta
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 28.05.2018
• Series: ACM Conferences
• Subjects: Big Data; Companies; Context Aware Privacy; Context modeling; Data models; Data privacy; Dataflow Applications; Privacy; Runtime; Security and privacy > Human and societal aspects of security and privacy > Privacy protections; Security and privacy > Software and application security > Domain-specific security and privacy architectures; Software and its engineering; data privacy; context-aware privacy; big data; dataflow applications
• ISBN: 9781450357159; 1450357156
• DOI: 10.1145/3194133.3194140

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.