Bluetooth familiarity: methods of calculation, applications and limitations

We present an approach for utilising a mobile device’s Bluetooth sensor to automatically identify social interactions and relationships between individuals in the real world. We show that a high degree of accuracy is achievable in the automatic identification of mobile devices of familiar individuals. This has implications for mobile device security, social networking and in context aware information access on a mobile device

Habitual Disclosure: Routine, Affordance and the Ethics of Young Peoples Social Media Data Surveillance

Drawing on findings from qualitative interviews and photo elicitation, this paper explores young people’s experiences of breaches of trust with social media platforms and how comfort is re-established despite continual violations. It provides rich qualitative accounts of users habitual relations with social media platforms. In particular, we seek to trace the process by which online affordances create conditions in which ‘sharing’ is regarded as not only routine and benign but pleasurable. Rather it is the withholding of data that is abnormalised. This process has significant implications for the ethics of data collection by problematising a focus on ‘consent’ to data collection by social media platforms. Active engagement with social media, we argue, is premised on a tentative, temporary, shaky trust that is repeatedly ruptured and repaired. We seek to understand the process by which violations of privacy and trust in social media platforms are remediated by their users and rendered ordinary again through everyday habits. We argue that the processes by which users become comfortable with social media platforms, through these routines, calls for an urgent reimagining of data privacy beyond the limited terms of consent

Trust Based Participant Driven Privacy Control in Participatory Sensing

Widespread use of sensors and multisensory personal devices generate a lot of personal information. Sharing this information with others could help in various ways. However, this information may be misused when shared with all. Sharing of information between trusted parties overcomes this problem. This paper describes a model to share information based on interactions and opinions to build trust among peers. It also considers institutional and other controls, which influence the behaviour of the peers. The trust and control build confidence. The computed confidence bespeaks whether to reveal information or not thereby increasing trusted cooperation among peers.Comment: 14 page

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

The Impact of Cultural Familiarity on Students’ Social Media Usage in Higher Education

Using social media (SM) in Higher education (HE) becomes unavoidable in the new teaching and learning pedagogy. The current generation of students creates their groups on SM for collaboration. However, SM can be a primary source of learning distraction due to its nature, which does not support structured learning. Hence, derived from the literature, this study proposes three learning customised system features, to be implemented on SM when used in Higher Education HE. Nevertheless, some psychological factors appear to have a stronger impact on students’ adoption of SM in learning than the proposed features. A Quantitative survey was conducted at a university in Uzbekistan to collect 52 undergraduate students’ perception of proposed SM learning customised features in Moodle. These features aim to provide localised, personalised, and privacy control self-management environment for collaboration in Moodle. These features could be significant in predicting students’ engagement with SM in HE. The data analysis showed a majority of positive feedback towards the proposed learning customised SM. However, the surveyed students’ engagement with these features was observed as minimal. The course leader initiated a semi-structured interview to investigate the reason. Although the students confirmed their acceptance of the learning customised features, their preferences to alternate SM, which is Telegram overridden their usage of the proposed learning customized SM, which is Twitter. The students avoided the Moodle integrated Twitter (which provided highly accepted features) and chose to use the Telegram as an external collaboration platform driven by their familiarity and social preferences with the Telegram since it is the popular SM in Uzbekistan. This study is part of an ongoing PhD research which involves deeper frame of learners’ cognitive usage of the learning management system. However, this paper exclusively discusses the cultural familiarity impact of student’s adoption of SM in HE

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar

Evaluating the Contextual Integrity of Privacy Regulation: Parents' IoT Toy Privacy Norms Versus COPPA

Increased concern about data privacy has prompted new and updated data protection regulations worldwide. However, there has been no rigorous way to test whether the practices mandated by these regulations actually align with the privacy norms of affected populations. Here, we demonstrate that surveys based on the theory of contextual integrity provide a quantifiable and scalable method for measuring the conformity of specific regulatory provisions to privacy norms. We apply this method to the U.S. Children's Online Privacy Protection Act (COPPA), surveying 195 parents and providing the first data that COPPA's mandates generally align with parents' privacy expectations for Internet-connected "smart" children's toys. Nevertheless, variations in the acceptability of data collection across specific smart toys, information types, parent ages, and other conditions emphasize the importance of detailed contextual factors to privacy norms, which may not be adequately captured by COPPA.Comment: 18 pages, 1 table, 4 figures, 2 appendice