The way in which IT systems are usually secured is through the use of username and password pairs. However, these credentials are all too easily lost, stolen or compromised. The use of behavioural biometrics can be used to supplement these credentials to provide a greater level of assurance in the identity of an authenticated user. However, user behaviours can also be used to ascertain other identifiable information about an individual. In this paper we build upon the notion of keystroke dynamics (the analysis of typing behaviours) to infer an anonymous user’s name and predict their native language. This work found that there is a discernible difference in the ranking of bigrams (based on their timing) contained within the name of a user and those that are not. As a result we propose that individuals will reliably type information they are familiar with in a discernibly different way. In our study we found that it should be possible to identify approximately a third of the bigrams forming an anonymous users name purely from how (not what) they type.

Advances in technology have made us as a society think more about cyber security and privacy, particularly how we consider and protect personal information. Such developments have introduced a temporal dimension to the definition of personal information and we have also witnessed new types of data emerging (e.g., phone sensor data, stress level measurements). These rapid technological changes introduce several challenges as legislation is often inadequate, and therefore questions regularly arise pertaining whether information should be considered personal or sensitive and thereby better protected. In this paper, therefore, we look to significantly advance research into this domain by investigating how personal information is regarded in governmental legislations/regulations, privacy policies of applications, and academic research articles. Through an assessment of how personal information has evolved and is perceived differently (e.g., in the context of sensitivity) across these key stakeholders, this work contributes to the understanding of the fundamental disconnects present and also the social implications of new technologies. Furthermore, we introduce a series of novel taxonomies of personal information which can significantly support and help guide how researchers and practitioners work with, or develop tools to protect, such information.

Smartphones and tablets are becoming ubiquitous within our connected lives and as a result these devices are increasingly being used for more and more sensitive applications, such as banking. The security of the information within these sensitive applications is managed through a variety of different processes, all of which minimise the exposure of this sensitive information to other potentially malicious applications. This paper documents experiments with the `zero-permission' motion sensors on the device as a side-channel for inferring the text typed into a sensitive application. These sensors are freely accessible without the phone user having to give permission. The research was able to, on average, identify nearly 30 percent of typed bigrams from unseen words, using a very small volume of training data, which was less than the size of a tweet. Given the natural redundancy in language this performance is often enough to understand the phrase being typed. We found that large devices were typically more vulnerable, as were users who held the device in one hand whilst typing with fingers. Of those bigrams which were not correctly identified over 60 percent of the errors involved the space bar and nearly half of the errors are within two keys on the keyboard.

The use of Big Data technologies and analytics have the potential to revolutionise the world. The mass instrumentation of the planet and society is providing intelligence that is not only enhancing our personal lives, but also opening up new opportunities for addressing some of key environmental, social and economic challenges of the 21st century. Unfortunately, as with all technology, there is the potential for misuse; in the case of personal data the ability to gather, enrich and mine at extreme pace and volume could result in societal-scale privacy intrusions. We apply a model for identity across cyber and physical spaces to the question of risk control for personal-data in the context of big data analytics. Using a graphical model for identity we reflect on the response options we have and how such risk controls may or may not be effective.

In this paper we propose a new approach for managing capability within organisations that are engaged in identity-attribution or identity-enrichment exercises. Specifically, we believe that a modelling framework which uses a bottom-up data-driven approach encapsulates the most appropriate abstraction of capability. In particular, that this should be agnostic (but aware) of the type of capability provider (whether a technology, service or a human) and that the capability framework be independent of specific staff or skill groups. In this way our approach could avoid the limitations of solely driving strategic capability enhancements through siloed role or skills-based staff pools, which are necessarily biased towards the staff management frameworks and potentially limit the strategic outcome.

The large number of millimetre and microwave terrestrial links that are in operation has lead to the belief that it may be possible to estimate two-dimensional rainfall rate fields. This paper outlines a reconstruction algorithm that can be used to estimate the rainfall field from a number of measurements of path attenuation on terrestrial links. In this paper we adopt a different approach to the regularization of this inverse problem. The main focus of the paper is a description of the retrieval algorithm which is demonstrated through simulation. The approach has been shown to be robust and relatively insensitive to errors and quantisation in the link measurements.