Alastair R. Beresford

Location privacy in pervasive computing

As location-aware applications begin to track our movements in the name of convenience, how can we protect our privacy? This article introduces the mix zone-a new construction inspired by anonymous communication techniques-together with metrics for assessing user anonymity. It is based on frequently changing pseudonyms.

Mix zones: user privacy in location-aware services

Privacy of personal location information is becoming an increasingly important issue. We refine a method, called the mix zone, developed to enhance user privacy in location-based services. We improve the mathematical model, examine and minimise computational complexity and develop a method of providing feedback to users.

Device Analyzer: Understanding Smartphone Usage

We describe Device Analyzer, a robust data collection tool which is able to reliably collect information on Android smartphone usage from an open community of contributors. We collected the largest, most detailed dataset of Android phone use publicly available to date. In this paper we systematically evaluate smartphones as a platform for mobile ubiquitous computing by quantifying access to critical resources in the wild. Our analysis of the dataset demonstrates considerable diversity in behaviour between users but also over time. We further demonstrate the value of handset-centric data collection by presenting case-study analyses of human mobility, interaction patterns, and energy management and identify notable differences between our results and those found by other studies.

Device analyzer: large-scale mobile data collection

We collected usage information from 12,500 Android devices in the wild over the course of nearly 2 years. Our dataset contains 53 billion data points from 894 models of devices running 687 versions of Android. Processing the collected data presents a number of challenges ranging from scalability to consistency and privacy considerations. We present our system architecture for collection and analysis of this highly-distributed dataset, discuss how our system can reliably collect time-series data in the presence of unreliable timing information, and discuss issues and lessons learned that we believe apply to many other big data collection projects.

Secure mobile computing via public terminals

The rich interaction capabilities of public terminals can make them more convenient to use than small personal devices, such as smart phones. However, the use of public terminals to handle personal data may compromise privacy. We present a system that enables users to access their applications and data securely using a combination of public terminals and a more trusted, personal device. Our system (i) provides users with capabilities to censor the public terminal display, so that it does not show private data; (ii) filters input events coming from the public terminal, so that maliciously injected keyboard/pointer events do not compromise privacy; and (iii) enables users to view personal information and perform data-entry via their personal device. A key feature of our system is that it works with unmodified applications. A prototype implementation of the system has been publicly released for Linux and Windows. The results arising from a pilot usability study based on this implementation are presented.

Spatial security policies for mobile agents in a sentient computing environment

A Sentient Computing environment is one in which the system is able to perceive the state of the physical world and use this information to customise its behaviour. Mobile agents are a promising new programming methodology for building distributed applications with many advantages over traditional client-server designs.We believe that properly controlled mobile agents provide a good foundation on which to build Sentient applications. The aims of this work are threefold: (i) to provide a simple location-based mechanism for the creation of security policies to control mobile agents; (ii) to simplify the task of producing applications for a pervasive computing environment through the constrained use of mobile agents; and (iii) to demonstrate the applicability of recent theoretical work using ambients to model mobility.

Intelligent Transportation Systems

This issues Works in Progress department features 10 interesting ongoing intelligent transportation systems projects. The first five projects (TIME, Sentient Transport, EVT, DynaCHINA, TrafficView) focus on traffic and vehicular data collection, transmission, and analysis. The sixth projects aims to provide intelligent-copilot services for driven, while the seventh focuses on asset identification and data collection for railroad environments. The eighth and ninth projects are budding application development environments for automobiles, and the tenth project is designing a multimodal biometric identification system for travel documents

Statistical modelling and analysis of sparse bus probe data in urban areas

Congestion in urban areas causes financial loss to business and increased use of energy compared with free-flowing traffic. Providing citizens with accurate information on traffic conditions can encourage journeys at times of low congestion and uptake of public transport. Installing the measurement infrastructure in a city to provide this information is expensive and potentially invades privacy. Increasingly, public transport vehicles are equipped with sensors to provide real-time arrival time estimates, but these data are sparse. Our work shows how these data can be used to estimate journey times experienced by road users generally. In this paper we describe (i) what a typical data set from a fleet of over 100 buses looks like; (ii) describe an algorithm to extract bus journeys and estimate their duration along a single route; (iii) show how to visualise journey times and the influence of contextual factors; (iv) validate our approach for recovering speed information from the sparse movement data.

Security Metrics for the Android Ecosystem

The security of Android depends on the timely delivery of updates to fix critical vulnerabilities. In this paper we map the complex network of players in the Android ecosystem who must collaborate to provide updates, and determine that inaction by some manufacturers and network operators means many handsets are vulnerable to critical vulnerabilities. We define the FUM security metric to rank the performance of device manufacturers and network operators, based on their provision of updates and exposure to critical vulnerabilities. Using a corpus of 20 400 devices we show that there is significant variability in the timely delivery of security updates across different device manufacturers and network operators. This provides a comparison point for purchasers and regulators to determine which device manufacturers and network operators provide security updates and which do not. We find that on average 87.7% of Android devices are exposed to at least one of 11 known critical vulnerabilities and, across the ecosystem as a whole, assign a FUM security score of 2.87 out of 10. In our data, Nexus devices do considerably better than average with a score of 5.17; and LG is the best manufacturer with a score of 3.97.

Survey of Technologies for the Implementation of National‐scale Road User Charging

Abstract This paper surveys the technologies available for constructing a pervasive, national‐scale road pricing system. It defines the different types of road pricing, the methods by which a vehicle’s position can be determined, and then examines possible pricing regimes in the context of their technological requirements and implications. The issue of enforcement and the distribution of pricing policies are considered, and further complexities are outlined. An examination of the security aspects of such systems is made, focusing particularly on the need to ensure privacy using technological, rather than solely procedural, methods. The survey concludes that a pervasive, national‐scale deployment is unlikely to be technically achievable in the short‐term.