Participatory sensing is emerging as an innovative computing paradigm that targets the ubiquity of always-connected mobile phones and their sensing capabilities. In this context, a multitude of pioneering applications increasingly carry out pervasive collection and dissemination of information and environmental data, such as, traffic conditions, pollution, temperature, etc. Participants collect and report measurements from their mobile devices and entrust them to the cloud to be made available to applications and users. Naturally, due to the personal information associated to the reports (e.g., location, movements, etc.), a number of privacy concerns need to be taken into account prior to a large-scale deployment of these applications. Motivated by the need for privacy protection in Participatory Sensing, this work presents PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of formal requirements aiming at protecting privacy of both data producers and consumers. We propose two instantiations that attain privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead.
Multiple Semi-Trusted Parties. Trust relations are difficult to define and set up in scenarios with multiple parties. Hence, it is advisable to minimize the number of trusted parties and the degree to which they are trusted. Available techniques to protect privacy in participatory sensing often involve many semi-trusted independent parties, that are always assumed not to collude. Anonysense @cite_17 , besides Mobile Nodes, Registration Authority, and WiFi Access Points, also assumes the presence and the non-collusion of a Task Service (used to distribute tasks to users), a Report Service (to receive reports from sensors), and several Mix Network nodes (i.e., a trusted anonymizing infrastructure). The assumption of multiple non-colluding parties raises severe concerns regarding its practicality and feasibility. It appears difficult to deploy all of the parties in a real world setting where entities provide services only in exchange of some benefit. For instance, it is not clear how to deploy the Task and the Report services as two separate entities having no incentive to collude.
@cite_17
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Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.