A Privacy-Preserving Social P2P Infrastructure for People-Centric Sensing

The rapid miniaturization and integration of sensor technologies into mobile Internet devices combined with Online Social Networks allows for enhanced sensor information querying, subscription, and task placement within People-Centric Sensing networks. However, PCS systems which exploit knowledge about OSN user profiles and context information for enhanced service provision might cause an unsolicited application and dissemination of highly personal and sensitive data. In this paper, we propose a protocol extension to our OSN design Vegas which enables secure, privacy-preserving, and trustful P2P communication between PCS participants. By securing knowledge about social links with standard public key cryptography, we achieve a degree of anonymity at a trust level which is almost good as that provided by a centralized trusted third party

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea