Privacy implications of online consumer-activity data: an empirical study

Web users allow online organisations to collect vast amounts of information about them and their activities, often in exchange for free services. But now there is a growing expectation that this users' data, generally called consumer data, should be given back to the users who helped create them so that it can be exploited for their benefit. In addition, there is a realisation that such a release of users' data could only promote greater transparency and accountability of organisations collecting them. As with any process where data is published, there is a risk that it could potentially lead to complex privacy issues. In this paper, we focus on what we believe is a significant and yet least explored data type: consumer-activity data, i.e., data (Web access logs) generated by an organisation which tracks the usage and interactions of its online services and resources. We conducted an exploratory qualitative study of 12 users to investigate what might be the consequences of making such consumer-activity data available to the users who generated them, especially its privacy challenges, both from an organisation's point of view and that of individuals whose online activities were being tracked. This was achieved by exposing the study's participants to a `personal analytics' dashboard which provided access to information on their usage and interactions with online systems of a large educational organisation (The Open University in the UK). The findings from our study showed that though there were potential benefits for the users, there were several privacy risks which are yet to be addressed

Distilling Mobile Privacy Requirements from Qualitative Data

As mobile computing applications have become commonplace, it is increasingly important for them to address end-users' privacy requirements. Mobile privacy requirements depend on a number of contextual socio-cultural factors to which mobility adds another level of contextual variation. However, traditional requirements elicitation methods do not sufficiently account for contextual factors and therefore cannot be used effectively to represent and analyse the privacy requirements of mobile end users. On the other hand, methods that investigate contextual factors tend to produce data which can be difficult to use for requirements modelling. To address this problem, we have developed a Distillation approach that employs a problem analysis model to extract and refine privacy requirements for mobile applications from raw data gathered through empirical studies involving real users. Our aim was to enable the extraction of mobile privacy requirements that account for relevant contextual factors while contributing to the software design and implementation process. A key feature of the distillation approach is a problem structuring framework called privacy facets (PriF). The facets in the PriF framework support the identification of privacy requirements from different contextual perspectives namely - actors, information, information-flows and places. The PriF framework also aids in uncovering privacy determinants and threats that a system must take into account in order to support the end-user's privacy. In this work, we first show the working of distillation using qualitative data taken from an empirical study which involved social-networking practices of mobile users. As a means of validating distillation, another distinctly separate qualitative dataset from a location-tracking study is used, in both cases, the empirical studies relate to privacy issues faced by real users observed in their mobile environment

A multi-pronged empirical approach to mobile privacy investigation

We describe the design of three empirical studies planned as part of an investigation into privacy when mobile. The studies exemplify complementary investigation strands, whose aim is to uncover the multi-faceted nature of privacy for mobile computing applications

Realizing networks of proactive smart products

The sheer complexity and number of functionalities embedded in many everyday devices already exceed the ability of most users to learn how to use them effectively. An approach to tackle this problem is to introduce ‘smart’ capabilities in technical products, to enable them to proactively assist and co-operate with humans and other products. In this paper we provide an overview of our approach to realizing networks of proactive and co-operating smart products, starting from the requirements imposed by real-world scenarios. In particular, we present an ontology-based approach to modeling proactive problem solving, which builds on and extends earlier work in the knowledge acquisition community on problem solving methods. We then move on to the technical design aspects of our work and illustrate the solutions, to do with semantic data management and co-operative problem solving, which are needed to realize our functional architecture for proactive problem solving in concrete networks of physical and resource-constrained devices. Finally, we evaluate our solution by showing that it satisfies the quality attributes and architectural design patterns, which are desirable in collaborative multi-agents systems

OUSocial2: a platform for gathering students’ feedback from social media

Universities strive to collect feedback from students to improve their courses and tutorship. Such feedback is often collected at the end of a course via survey forms. However, such methods in collecting feedback are too controlled, slow, and passive. With the rise of social media, many students are finding online venues to group and share their experiences and seek peers’ support. OUSocial2 is a platform that monitors behaviour, sentiment, and topics, in open social media groups set up by, and for, Open University students. It captures anonymous feedback from students towards their courses, and tracks the evolution of engagement behaviour and sentiment within those groups

Effects of perceived stress, mindfulness, selfefficacy and social support on psychological wellbeing of life insurance agents during the COVID-19 pandemic

The Conservation of Resources theory has been set in motion to understand the psychological wellbeing at work-place-focused foothold of the realm in light of the JD-R theory. Life insurance agents experience multifarious stressors and challenges that negatively impact their psychological wellbeing. The current pandemic situation of the COVID-19 outbreak has directed significance to workplace health promotion as a novel postulation addressed in this study. This research is the first to empirically test and investigate the predicting effects of perceived stress, mindfulness, social support, and self-efficacy on psychological well-being among 794 Life Insurance Agents in India. This non-experimental research method incorporates the reflective model analysed through Smart PLS-3. A power analysis is executed by drawing evidence from India recruited through random sampling. Results show mindfulness as the strongest and most effective predictor of positive psychological well-being. This study underpins the significance of mindfulness-based interventions in unprecedented times during the COVID-19 pandemic where the mindful selling of the right policies surges and assists the agents to build a long-term relationship with the customers. Future studies should try to test these interventions with multi-centred research that can further enhance the robustness of research findings

Dealing with diversity in a smart-city datahub

In this paper, we present the data curation approach taken by the MK:Smart project, creating a large data repository of datasets about all aspects of the city of Milton Keynes in the UK and its citizens. The issues faced here, which we believe will become more and more common to large, data-centric smart-cities initiatives, is the one associated with the diversity of these thousands of datasets in terms of the licenses, policies and terms they are associated with them. We describe this repository of datasets, the MK Datahub, and its architecture to create data workflows from original sources to applications. We focus on the approach taken to record, in a structured, ontology-based way the components of the licenses and policies of each dataset, as well as the tools we are developing to manage such representations and to reason with them

Contravision: Exploring users' reactions to futuristic technology

How can we best explore the range of users' reactions when developing future technologies that maybe controversial, such as personal healthcare systems? Our approach – Contravision – uses futuristic videos, or other narrative forms, that convey either negative or positive aspects of the proposed technology for the same scenarios. We conducted a users study to investigate what range of responses the different versions elicited. Our findings show that the use of two systematically comparable representations of the same technology can elicit a wider spectrum of reactions than a single representation can. We discuss why this is so and the value of obtaining breadth in user feedback for potentially controversial technologies

Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: Stability and moving-horizon approximations

Stability results are given for a class of feedback systems arising from the regulation of time-varying discrete-time systems using optimal infinite-horizon and moving-horizon feedback laws. The class is characterized by joint constraints on the state and the control, a general nonlinear cost function and nonlinear equations of motion possessing two special properties. It is shown that weak conditions on the cost function and the constraints are sufficient to guarantee uniform asymptotic stability of both the optimal infinite-horizon and moving-horizon feedback systems. The infinite-horizon cost associated with the moving-horizon feedback law approaches the optimal infinite-horizon cost as the moving horizon is extended.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45231/1/10957_2004_Article_BF00938540.pd