Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context ofGreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in[1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

Denial-of-service (DoS) attacks are considered within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting, it is shown that clients can adapt effectively to an attack by increasing their request rate based on timeout windows to estimate attack rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. The protocol introduced here, called Adaptive Selective Verification (ASV), is shown to use bandwidth efficiently and does not require any server state or assumptions about network congestion. The main results of the paper are a formulation of optimal performance and a proof that ASV is optimal.

We consider reliable telemetry in white spaces in the form of protecting the integrity of distributed spectrum measurements against coordinated misreporting attacks. Our focus is on the case where a subset of the sensors can be remotely attested. We propose a practical framework for using statistical sequential estimation coupled with machine learning classifiers to deter attacks and achieve quantifiably precise outcome. We provide an application-oriented case study in the context of spectrum measurements in the white spaces. The study includes a cost analysis for remote attestation, as well as an evaluation using real transmitter and terrain data from the FCC and NASA for Southwest Pennsylvania. The results show that with as low as 15% penetration of attestation-capable nodes, more than 94% of the attempts from omniscient attackers can be thwarted.

Attribute-Based Messaging (ABM) enables messages to be addressed using attributes of recipients rather than an explicit list of recipients. Such messaging offers benefits of efficiency, exclusiveness, and intensionality, but faces challenges in access control and confidentiality. In this article we explore an approach to intraenterprise ABM based on providing access control and confidentiality using information from the same attribute database exploited by the addressing scheme. We show how to address three key challenges. First, we demonstrate a manageable access control system based on attributes. Second, we demonstrate use of attribute-based encryption to provide end-to-end confidentiality. Third, we show that such a system can be efficient enough to support ABM for mid-size enterprises. Our implementation can dispatch confidential ABM messages approved by XACML policy review for an enterprise of at least 60,000 users with only seconds of latency.

We consider Denial of Service (DoS) attacks within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting it is shown that the clients can respond effectively to an attack by using bandwidth as a payment scheme and time-out windows to adaptively boost request rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. Our protocol, which we call Adaptive Selective Verification (ASV) is shown to be efficient in terms of bandwidth consumption using both a theoretical model and network simulations. It differs from previously-investigated adaptive mechanisms for bandwidth-based payment by requiring very limited state on the server.

Constraint systems provide techniques for automatically analyzing the conformance of low-level access control policies to high-level business rules formalized as logical constraints. However, there are likely to be priorities for solutions that are not easy to encode formally, so administrator input is often important. This paper introduces PolicyMorph, a constraint system that supports interactive development and maintenance of access control policies that respect both formalized and un-formalized business rules and priorities. We provide a mathematical description of the system and an architecture for implementing it. We constructed a prototype that is validated using a case study in which constraints are imposed on a building automation system that controls door locks. PolicyMorph advances the state-of-the-art in constraint systems by suggesting predictable policy model modifications that will resolve specific constraint violations and then allowing policy administrators to select the appropriate modifications using knowledge that is not formally encoded in the constraint system.

Attribute based messaging (ABM) enables message senders to dynamically create a list of recipients based on their attributes as inferred from an enterprise database. Such targeted messaging can reduce unnecessary communications and enhance privacy, but faces challenges in access control. In this paper, we explore an approach to ABM based on deriving access control information from the same attribute database exploited by the addressing scheme. We show how to address three key challenges. First, we demonstrate a manageable access control system based on attributes. Second we show how this can be used with existing messaging systems to provide a practical deployment strategy. Third, we show that such a system can be efficient enough to support ABM for mid-size enterprises. Our implementation can dispatch ABM messages approved by XACML review for an enterprise of at least 60,000 users with only seconds of latency