Sensor data is a core component of big data. The abundance of sensor data combined with advances in data integration and data mining entails a great opportunity to develop innovative applications. However, data about our movements, our energy consumption or our biometry are personal data that we should have full control over. Like- wise, companies face a trade-off as the benefits of innovative services must be weighted against the risk of exposing data that reveal core in- ternal processes. How to design a data platform that enables innovative data services and yet enforce access and usage control? The solutions pro- posed in the literature to this trade-off all involve some form of trusted execution environment, where data and processing is trusted and safe from corruption by users or attackers. The hardware that could support such trusted execution environments is however closed to the research community: OEMs disable security extensions from their development boards and the software handling these security extensions is not open. In this paper we present a framework that combines commercially avail- able hardware and open source software. It can be used today by the research community as a trusted execution environment to investigate future big data platforms.
Lecture Notes in Computer Science, 2013, p. 458-467