Stephen Hailes

A distributed trust model

The widespread use of the Internet signals the need for a better understanding of trust as a basis for secure on-line interaction. In the face of increasing uncertainty and risk, users must be allowed to reason flectively about the trustworthiness of on-line entities. In this paper, we outline the shortcomings of current security approaches for managing trust and propose a model for trust, based on distributed recommendations.

Adaptive routing for intermittently connected mobile ad hoc networks

The vast majority of mobile ad hoc networking research makes a very large assumption - that communication can only take place between nodes that are simultaneously accessible within the same connected cloud (i.e., that communication is synchronous). In reality, this assumption is likely to be a poor one, particularly for sparsely or irregularly populated environments. We present the context-aware routing (CAR) algorithm. CAR is a novel approach to the provision of asynchronous communication in partially-connected mobile ad hoc networks, based on the intelligent placement of messages. We discuss the details of the algorithm, and then present simulation results demonstrating that it is possible for nodes to exploit context information in making local decisions that lead to good delivery ratios and latencies with small overheads.

Temporal diversity in recommender systems

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the systems top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.

An ad hoc mobility model founded on social network theory

Almost all work on mobile ad hoc networks relies on simulations, which, in turn, rely on realistic movement models for their credibility. Since there is a total absence of realistic data in the public domain, synthetic models for movement pattern generation must be used and the most widely used models are currently very simplistic, the focus being ease of implementation rather than soundness of foundation. Whilst it would be preferable to have models that better reflect the movement of real users, it is currently impossible to validate any movement model against real data. However, it is lazy to conclude from this that all models are equally likely to be invalid so any will do.We note that movement is strongly affected by the needs of humans to socialise in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled, and that are likely to bias their movement patterns. Thus, we propose a new mobility model that is founded on social network theory, because this has empirically been shown to be useful as a means of describing human relationships. In particular, the model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is only then mapped to a topographical space, with topography biased by the strength of social tie.We discuss the implementation of this mobility model and we evaluate emergent properties of the generated networks. In particular, we show that grouping mechanism strongly influences the probability distribution of the average degree (i.e., the average number of neighbours of a host) in the simulated network.

B-Trust: bayesian trust framework for pervasive computing

Without trust, pervasive devices cannot collaborate effectively, and without collaboration, the pervasive computing vision cannot be made a reality. Distributed trust frameworks may support trust and thus foster collaboration in an hostile pervasive computing environment. Existing frameworks deal with foundational properties of computational trust. We here propose a distributed trust framework that satisfies a broader range of properties. Our framework: (i) evolves trust based on a Bayesian formalization, whose trust metric is expressive, yet tractable; (ii) is lightweight; (iii) protects user anonymity, whilst being resistant to “Sybil attacks” (and enhancing detection of two collusion attacks); (iv) integrates a risk-aware decision module. We evaluate the framework through four experiments.

Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy.

Upwash exploitation and downwash avoidance by flap phasing in ibis formation flight.

Many species travel in highly organized groups. The most quoted function of these configurations is to reduce energy expenditure and enhance locomotor performance of individuals in the assemblage. The distinctive V formation of bird flocks has long intrigued researchers and continues to attract both scientific and popular attention. The well-held belief is that such aggregations give an energetic benefit for those birds that are flying behind and to one side of another bird through using the regions of upwash generated by the wings of the preceding bird, although a definitive account of the aerodynamic implications of these formations has remained elusive. Here we show that individuals of northern bald ibises (Geronticus eremita) flying in a V flock position themselves in aerodynamically optimum positions, in that they agree with theoretical aerodynamic predictions. Furthermore, we demonstrate that birds show wingtip path coherence when flying in V positions, flapping spatially in phase and thus enabling upwash capture to be maximized throughout the entire flap cycle. In contrast, when birds fly immediately behind another bird—in a streamwise position—there is no wingtip path coherence; the wing-beats are in spatial anti-phase. This could potentially reduce the adverse effects of downwash for the following bird. These aerodynamic accomplishments were previously not thought possible for birds because of the complex flight dynamics and sensory feedback that would be required to perform such a feat. We conclude that the intricate mechanisms involved in V formation flight indicate awareness of the spatial wake structures of nearby flock-mates, and remarkable ability either to sense or predict it. We suggest that birds in V formation have phasing strategies to cope with the dynamic wakes produced by flapping wings.

Power conservation strategy for mobile computers using load sharing

Power management is an important aspect of mobile computing. Previous works on power conservation have concentrated on the hardware approach. In this paper, we propose a different approach of power conservation strategy for mobile computers which is based on the concept of load sharing. User jobs are transferred from a mobile host to a fixed host to reduce power consumption by the CPU. Simulation results show that under suitable conditions, transferring job can extend battery lifetime by up to 20%. Transferring jobs to a fixed host does not only extend battery lifetime but also gives users access to faster machines, hence improving job response time.

Bringing security home: a process for developing secure and usable systems

The aim of this paper is to provide better support for the development of secure systems. We argue that current development practice suffers from two key problems:1. Security requirements tend to be kept separate from other system requirements, and not integrated into any overall strategy.2. The impact of security measures on users and the operational cost of these measures on a day-to-day basis are usually not considered.Our new paradigm is the full integration of security and usability concerns into the software development process, thus enabling developers to build secure systems that work in the real world. We present AEGIS, a secure software engineering method which integrates asset identification, risk and threat analysis and context of use, bound together through the use of UML, and report its application to case studies on Grid projects. An additional benefit of the method is that the involvement of stakeholders in the high-level security analysis improves their understanding of security, and increases their motivation to comply with policies.

Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue

Collaborative applications for co-located mobile users can be severely disrupted by a sybil attack to the point of being unusable. Existing decentralized defences have largely been designed for peer-to-peer networks but not for mobile networks. That is why we propose a new decentralized defence for portable devices and call it MobID. The idea is that a device manages two small networks in which it stores information about the devices it meets: its network of friends contains honest devices, and its network of foes contains suspicious devices. By reasoning on these two networks, the device is then able to determine whether an unknown individual is carrying out a sybil attack or not. We evaluate the extent to which MobID reduces the number of interactions with sybil attackers and consequently enables collaborative applications. We do so using real mobility and social network data. We also assess computational and communication costs of MobID on mobile phones.