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.
Other proposals, such as @cite_29 and @cite_41 , aim at guaranteeing integrity and authenticity of user-generated contents, by employing Trusted Platform Modules (TPMs).
@cite_41 @cite_29
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Commodity mobile devices have been utilized as sensor nodes in a variety of domains, including citizen journalism, mobile social services, and domestic eldercare. In each of these domains, data integrity and device-owners' privacy are first-class concerns, but current approaches to secure sensing fail to balance these properties. External signing infrastructure cannot attest to the values generated by a device's sensing hardware, while trusted sensing hardware does not allow users to securely reduce the fidelity of readings in order to preserve their privacy. In this paper we examine the challenges posed by the potentially conflicting goals of data integrity and user privacy and propose a trustworthy mobile sensing platform which leverages inexpensive commodity Trusted Platform Module (TPM) hardware. Grassroots Participatory Sensing empowers people to collect and share sensor data using mobile devices across many applications, spanning intelligent transportation, air quality monitoring and social networking. In this paper, we argue that the very openness of such a system makes it vulnerable to abuse by malicious users who may poison the information, collude to fabricate information, or launch Sybils to distort that information. We propose and implement a novel trusted platform module (TPM), or angel based system that addresses the problem of providing sensor data integrity. The key idea is to provide a trusted platform within each sensor device to attest the integrity of sensor readings. We argue that this localizes integrity checking to the device, rather than relying on corroboration, making the system not only simpler, but also resistant to collusion and data poisoning. A "burned-in" private key in the TPM prevents users from launching Sybils. We also make the case for content protection and access control mechanisms that enable users to publish sensor data streams to selected groups of people and address it using broadcast encryption techniques.