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
2146697949 2109784850 202035697 2114902045
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. The ubiquity of mobile devices has brought forth the concept of participatory sensing, whereby ordinary citizens can now contribute and share information from the urban environment. However, such applications introduce a key research challenge: preserving the privacy of the individuals contributing data. In this paper, we study two different privacy concepts, k-anonymity and l-diversity, and demonstrate how their privacy models can be applied to protect users' spatial and temporal privacy in the context of participatory sensing. The first part of the paper focuses on schemes implementing k-anonymity. We propose the use of microaggregation, a technique used for facilitating disclosure control in databases, as an alternate to tessellation, which is the current state-of-the-art for location privacy in participatory sensing applications. We conduct a comparative study of the two techniques and demonstrate that each has its advantage in certain mutually exclusive situations. We then propose the Hybrid Variable size Maximum Distance to Average Vector (Hybrid-VMDAV) algorithm, which combines the positive aspects of microaggregation and tessellation. The second part of the paper addresses the limitations of the k-anonymity privacy model. We employ the principle of l-diversity and propose an l-diverse version of VMDAV (LD-VMDAV) as an improvement. In particular, LD-VMDAV is robust in situations where an adversary may have gained partial knowledge about certain attributes of the victim. We evaluate the performances of our proposed techniques using real-world traces. Our results show that Hybrid-VMDAV improves the percentage of positive identifications made by an application server by up to 100 and decreases the amount of information loss by about 40 . We empirically show that LD-VMDAV always outperforms its k-anonymity counterpart. In particular, it improves the ability of the applications to accurately interpret the anonymized location and time included in user reports. Our studies also confirm that perturbing the true locations of the users with random Gaussian noise can provide an extra layer of protection, while causing little impact on the application performance. The ubiquity of the Internet has led to increased resource sharing between large numbers of users in widely-disparate administrative domains. Unfortunately, traditional identity-based solutions to the authorization problem do not allow for the dynamic establishment of trust, and thus cannot be used to facilitate interactions between previously-unacquainted parties. Furthermore, the management of identity-based systems becomes burdensome as the number of users in the system increases. To address this gap between the needs of open computing systems and existing authorization infrastructures, researchers have begun to investigate novel attribute-based access control (ABAC) systems based on techniques such as trust negotiation and other forms of distributed proving. To date, research in these areas has been largely theoretical and has produced many important foundational results. However, if these techniques are to be safely deployed in practice, the systems-level barriers hindering their adoption must be overcome. In this thesis, we show that safely and securely adopting decentralized ABAC approaches to authorization is not simply a matter of implementation and deployment, but requires careful consideration of both formal properties and practical issues. To this end, we investigate a progression of important questions regarding the safety analysis, deployment, implementation, and optimization of these types of systems. We first show that existing ABAC theory does not properly account for the asynchronous nature of open systems, which allows attackers to subvert these systems by forcing decisions to be made using inconsistent system states. To address this, we develop provably-secure and lightweight consistency enforcement mechanisms suitable for use in trust negotiation and distributed proof systems. We next focus on deployment issues, and investigate how user interactions can be audited in the absence of concrete user identities. We develop the technique of virtual fingerprinting, which can be used to accomplish this task without adversely affecting the scalability of audit systems. Lastly, we present TrustBuilder2, which is the first fully-configurable framework for trust negotiation. Within this framework, we examine availability problems associated with the trust negotiation process and develop a novel approach to policy compliance checking that leverages an efficient pattern-matching approach to outperform existing techniques by orders of magnitude. We argue that the traditional notion of trust as a relation among entities, while useful, becomes insufficient for emerging data-centric mobile ad hoc networks. In these systems, setting the data trust level equal to the trust level of the data- providing entity would ignore system salient features, rendering applications ineffective and systems inflexible. This would be even more so if their operation is ephemeral, i.e., characterized by short-lived associations in volatile environments. In this paper, we address this challenge by extending the traditional notion of trust to data-centric trust: trustworthiness attributed to node-reported data per se. We propose a framework for data-centric trust establishment: First, trust in each individual piece of data is computed; then multiple, related but possibly contradictory, data are combined; finally, their validity is inferred by a decision component based on one of several evidence evaluation techniques. We consider and evaluate an instantiation of our framework in vehicular networks as a case study. Our simulation results show that our scheme is highly resilient to attackers and converges stably to the correct decision.