[1] . Technical Report 2014/01, April 2014. [ bib | www: ]

[2] Armstrong Nhlabatsi, Thein Tun, Niamul Khan, Yijun Yu, Arosha Bandara, Khaled Khan, and Bashar Nuseibeh. Enriching Traceability with Context for Adaptive Information Security in the Cloud. Technical Report 2014/02, April 2014. [ bib | .pdf ]
Cloud applications enjoy a diverse community of users to store and process a variety of data in different conditions in their execution environment. We refer to the attributes that determine these conditions as context. Therefore these applications have a variety of security requirements, the satisfaction of which depends on the application adapting on the users’ context. We call such adaptation capability Adaptive Information Security. The paper argues that one of the key prerequisites for adaptive information security in the cloud is the use of traceability as a means to reasoning the relationship between security requirements and the policies that satisfy those requirements. However, current approaches to traceability do not provide support for taking into account contextual attributes. This makes it challenging to reason about satisfaction of the security requirement at runtime. We propose an approach to traceability that addresses this challenge by making context explicit. Our approach uses entailment relationships to capture and enrich traceability links with context. We use these links to diagnose the violation of security requirements. We applied our approach to an open-source cloud application (ownCloud) which we re-engineered for adaptive access control.

[3] Mu Yang, Yijun Yu, Arosha K. Bandara, and Bashar Nuseibeh. Adaptive Sharing for Online Social Networks: A Trade-off between Privacy Risk and Social Benefit. Technical Report 2014/03, August 2014. [ bib | .pdf ]
Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Users thus usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in the context. This problem drives the need for a better privacy control which can adaptively filter the members in the selected social circle to satisfy users' requirements and meanwhile maintain users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e., potential privacy risks) and incentives of sharing (i.e., potential social benefit), and quantifies users' requirements as a trade-off between these two types of utilities. By balancing these two metrics, our framework suggests a subset of the selected circle aiming to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.

[4] An analysis of algorithmic composition interaction design with reference to Cognitive Dimensions. Matt Bellingham and Simon Holland and and Paul Mulholland. Technical Report 2014/04, May 2014. [ bib | .pdf ]
This paper presents an analysis, using Cognitive Dimensions (Blackwell and Green, 2003), of a representative selection of user interfaces for algorithmic composition software. Cognitive Dimensions are design principles for notations, user interfaces and programming language design, or from another viewpoint 'discussion tools' for designers (Green & Blackwell, 1998). For the purposes of this report, algorithmic composition software is software which generates music using computer algorithms, where the algorithms may be controlled by end users (who may variously be considered as composers or performers). For example, the algorithms may be created by the end user, or the user may provide data or parameter settings to pre-existing algorithms. Other kinds of end-user manipulation are also possible. A wide variety of algorithmic composition software is considered, including visual programming languages, text-oriented programming languages, and software which requires or allows data entry by the user. The paper considers a representative, rather than comprehensive, selection of software. The analysis also draws, where appropriate, on related discussion tools drawn from Crampton Smith (Moggridge, 2006), Cooper et al. (2007) and Rogers et al. (2011). Finally, the paper reflects on the compositional representation of time as a critical dimension of composition software that is not satisfactorily addressed by Cognitive Dimensions, or any of the other discussion tools.

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