Opportunistic sensing: Security challenges for the new paradigm

Abstract

We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people-not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic peoplecentric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm.

The rise of low-cost sensing for managing air pollution in cities

“…In fact, low-cost sensing has created the idea of so-called opportunistic sensing, which is using the data collected for one purpose for multiple other purposes as well (Campbell et al 2008). Anthropocentric opportunistic sensing involves the collection, storage, processing and fusing of large volumes of data related to everyday human activities carried out by the general public in highly dynamic and mobile urban settings (Kapadia et al 2009). Such datasets are highly useful to environmental health scientists and epidemiologists for gaining unparalleled insight into environmental drivers of individual and community health (White et al 2012).…”

Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

“…The privacy concerns in crowdsensing were first pointed out in [57], immediately followed by [58]. AnonySense [28], a privacy preserving crowdsensing framework offers strong privacy protection at the data collection server.…”

Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp

“…By using small computational devices carried by individuals in their daily activities, information related to human activity and to the environment around them can be sensed opportunistically (Campbell et al, 2008;Kapadia et al, 2009;Kumar et al, 2015). As the data originates from sensors carried by people, new challenges for information security and privacy have to be addressed (Kapadia et al, 2009). However, in this paper, we do not restrict it to people and focus more on the uncontrolled aspect of the data collection and its implications for the processing and interpretation of the results.…”