Participatory Sensing: The Tension Between Social Translucence and Privacy

Abstract

Participatory sensing is a new research area that emerged from the need to complement our previous efforts in wireless sensor networks. It takes advantage of the emergence of rich-sensor mobile phones and their wide adoption, in order to turn people to producers of sensed data and enable new classes of collective applications. Unavoidably, this raises a lot of privacy concerns, as people are becoming sensors and give out a lot of personal information, like their location. If we choose to protect their privacy by anonymizing the data and completely hiding any identifying information, then the visibility of their contributions to others is lost. However, it is important to maintain this property, in order to support accountability on one hand and allow people gain reputation for their efforts on the other hand. In this book chapter we investigate which of the available technical solutions we need, in order to resolve this conflict and what are the research directions that emerge.

Optimized Reputable Sensing Participants Extraction for Participatory Sensor Networks

“…For some networks, such as Epinions, CA CondMat, CA HepPh, and Slashdot, even the ratio of sensing participants whose reputation is bigger than 0.1 is very limited. This is very different from the intuitive thought in some reputation based applications [6,12]: they usually use those whose reputation is big in their model. However, Figure 3, the powers of the reputation distributions in simulation networks are summarized in Table 2.…”

“…Combining the power of crowd, the ubiquitously available smart devices, and the high speed Internet, participatory sensor networks have raised more and more attention nowadays [1][2][3]. Participants use their personal smart devices, which have embedded sensors such as camera, microphone, GPS, accelerometer, digital compass, and gyroscope, to gather data and make them available for large-scale applications [4][5][6]. The performance and the efficiency of participatory sensing applications heavily depend on the quality of data contributed by the sensing participants [7,8].…”

IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications

“…We compare IncogniSense with existing anonymity-preserving reputation systems designed for application domains orthogonal to participatory sensing, as this specific problem has not been addressed in the context of participatory sensing, other than a mention of its importance as future research [8]. To the best of our knowledge, we are the first to propose a concrete framework for this paradigm including a proof-of-concept implementation and a multi-dimensional evaluation.…”

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

“…However, if monetary incentive was to be used instead in the Boston Marathon bombing case, privacy preservation or anonymization becomes really difficult, particularly because of the need to accurately identify and reward users who have contributed relevant data. Krontiris and Maisonneuve [56] describe this phenomenon as the tension between social translucence and privacy. For similar reason to accurately identify and incentivise citizens with monetary rewards, the CityZen crowdsensing solution requires registration and authentication of users, potentially minimising the level of privacy protection anonymous participants would otherwise enjoy [57].…”