End-to-End Privacy for Open Big Data Markets

The idea of an open data market envisions the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviours of data owners and to generate additional business value using different techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This paper discusses why privacy matters in the IoT domain in general and especially in open data markets and surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end to end privacy for open data markets. We also highlight some of the major research challenges that need to be address in order to make the vision of open data markets a reality through ensuring the privacy of stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special Issue Cloud Computing and the La

Reconceptualising public spaces of (IN)equality: sensing and creating layers of visibility

Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Geografia e Planeamento Territorial - Especialidade: Geografia HumanaSpace and social identities mutual relation of constitution and reproduction lead us to the understanding that space reflects power relations and hegemonic discourses, and that inequality can perpetuate itself through the ways space is organized, experienced, represented and created. Public spaces are constructed around particular notions of appropriate sexual comportment, reflecting and reproducing heteronormativity, as they exclude non-normative sexualities, such as lesbian sexualities. In a context of a heteronormative socio-spatial landscape women can decide not to disclose their non-normative sexual orientation, leading to a pervasive invisibility of lesbian sexualities in public spaces. Concurrently the pervasive invisibility of lesbian sexualities in public spaces reinforces power inequalities, feeding back the heteronormative socio-spatial landscape. Discrimination on grounds of sexual orientation is still a widespread reality in Portugal in spite of the significant legal advances towards equality in recent years. Discriminated minority groups, such as lesbians, experience power inequalities in their everyday lives, and their spatial invisibility in public spaces contributes to their disempowerment. Communication technologies recast the organization and production of the spatial and temporal scenes of social life and they open new possibilities of public action. The production of alternative representations of space, based on individuals’ georeferenced experiences, thoughts and emotions are increasingly supported by the potentialities of Internet based technologies, such as the ever more easy-to-use online software. The potential of these technologies to promote the agency, to change power relations and to disrupt the hegemonic discourse increase as more people become the authors of a complementary flow of knowledge, information, memories and stories. This research explores the potential of geospatial online practices, based upon the experiences, emotions and feelings of lesbian and bisexual women to disclose the socially encoded meanings of different bodies in specific spatial, temporal and cultural contexts, highlighting how spaces and sexual identities are mutually constitutive. This research project aims to explore the potential of collaborative web mapping to promote the agency and empowerment of lesbian and bisexual women. It is structured in three phases: ‘Mapping the landscape’ aims to map spaces of lesbian and gay visibility in public spaces to contextualise the hetero pervasive reality in Portugal; the second phase ‘Sensing the landscape’ focuses on the intersections of gender and sexual orientation, aiming to identify significant dimensions of space and places that relate to lesbian and bisexual women sexual identities; and the third and final phase of the research ‘Creating landscapes’ explores how creating and sharing digital layers of lesbian visibility on collaborative web maps can disrupt a hetero pervasive reality and impact social identity and belonging, building capacities for action of lesbian and bisexual women, and facilitating same-sex public displays of affection. Ultimately, this research aims to explore the empowering potentialities of geospatial online practices to provide alternative possibilities for citizenship, and foster social change

WISETales: Designing a New Niche Online Community for Women in Science and Engineering to Share Personal Stories

User contributions are vital to online communities; therefore it is important to know how to motivate user participation to ensure flow and quality of content, and to generate more traffic and revenue to community owners. In contrast to previous research which has explored the motivations of participants in already existing online communities, I investigate whether a new niche online community with a particular focus(women in Science and Engineering sharing their personal experiences through stories) can be started through a design that follows best practices for community design and principles derived from theories of motivation. The design of the WISETales community is based upon insights from literature in three main areas: social psychology, computer science, and gender studies. A social visualization which serves informational, navigational and motivational tool was also developed. One pilot study and two exploratory studies were carried out to evaluate the need for such a community, its design and interface usability. The design of the community and visualization, along with the results from the studies, their analysis and discussion are presented in the thesis

Exploring Societal Computing based on the Example of Privacy

Data privacy when using online systems like Facebook and Amazon has become an increasingly popular topic in the last few years. This thesis will consist of the following four projects that aim to address the issues of privacy and software engineering. First, only a little is known about how users and developers perceive privacy and which concrete measures would mitigate their privacy concerns. To investigate privacy requirements, we conducted an online survey with closed and open questions and collected 408 valid responses. Our results show that users often reduce privacy to security, with data sharing and data breaches being their biggest concerns. Users are more concerned about the content of their documents and their personal data such as location than about their interaction data. Unlike users, developers clearly prefer technical measures like data anonymization and think that privacy laws and policies are less effective. We also observed interesting differences between people from different geographies. For example, people from Europe are more concerned about data breaches than people from North America. People from Asia/Pacific and Europe believe that content and metadata are more critical for privacy than people from North America. Our results contribute to developing a user-driven privacy framework that is based on empirical evidence in addition to the legal, technical, and commercial perspectives. Second, a related challenge to above, is to make privacy more understandable in complex systems that may have a variety of user interface options, which may change often. As social network platforms have evolved, the ability for users to control how and with whom information is being shared introduces challenges concerning the configuration and comprehension of privacy settings. To address these concerns, our crowd sourced approach simplifies the understanding of privacy settings by using data collected from 512 users over a 17 month period to generate visualizations that allow users to compare their personal settings to an arbitrary subset of individuals of their choosing. To validate our approach we conducted an online survey with closed and open questions and collected 59 valid responses after which we conducted follow-up interviews with 10 respondents. Our results showed that 70% of respondents found visualizations using crowd sourced data useful for understanding privacy settings, and 80% preferred a crowd sourced tool for configuring their privacy settings over current privacy controls. Third, as software evolves over time, this might introduce bugs that breach users' privacy. Further, there might be system-wide policy changes that could change users' settings to be more or less private than before. We present a novel technique that can be used by end-users for detecting changes in privacy, i.e., regression testing for privacy. Using a social approach for detecting privacy bugs, we present two prototype tools. Our evaluation shows the feasibility and utility of our approach for detecting privacy bugs. We highlight two interesting case studies on the bugs that were discovered using our tools. To the best of our knowledge, this is the first technique that leverages regression testing for detecting privacy bugs from an end-user perspective. Fourth, approaches to addressing these privacy concerns typically require substantial extra computational resources, which might be beneficial where privacy is concerned, but may have significant negative impact with respect to Green Computing and sustainability, another major societal concern. Spending more computation time results in spending more energy and other resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable - systems where privacy could be achieved "for free", i.e., without having to spend extra computational effort. We describe how privacy can indeed be achieved for free an accidental and beneficial side effect of doing some existing computation - in web applications and online systems that have access to user data. We show the feasibility, sustainability, and utility of our approach and what types of privacy threats it can mitigate. Finally, we generalize the problem of privacy and its tradeoffs. As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole

Developing and Facilitating Temporary Team Mental Models Through an Information-Sharing Recommender System

It is well understood that teams are essential and common in many aspects of life, both work and leisure. Due to the importance of teams, much research attention has focused on how to improve team processes and outcomes. Of particular interest are the cognitive aspects of teamwork including team mental models (TMMs). Among many other benefits, TMMs involve team members forming a compatible understanding of the task and team in order to more efficiently make decisions. This understanding is sometimes classified using four TMM domains: equipment (e.g., operating procedures), task (e.g., strategies), team interactions (e.g., interdependencies) and teammates (e.g., tendencies). Of particular interest to this dissertation is accelerating the development of teammate TMMs which include members understanding the knowledge, skills, attitudes, preferences, and tendencies of their teammates. An accurate teammate TMM allows teams to predict and account for the needs and behaviors of their teammates. Although much research has highlighted how the development of the four TMM domains can be supported, promoting the development of teammate TMMs is particularly challenging for a specific type of team: temporary teams. Temporary teams, in contrast to ongoing teams, involve unknown teammates, novel tasks, short task times (alternatively limited interactions), and members disbanding after completing their task. These teams are increasingly used by organizations as they can be agilely formed with individual members selected to accomplish a specific task. Such teams are commonly used in contexts such as film production, the military, emergency response, and software development, just to name a few. Importantly, although these teams benefit greatly from teammate TMMs due to the efficiencies gained in decision making while working under limited deadlines, the literature is severely limited in understanding how to support temporary teams in this way. As prior research has suggested, an opportunity to accelerate teammate TMM development on temporary teams is through the use of technology to selectively share teammate information to support these TMMs. However, this solution poses numerous privacy concerns. This dissertation uses four studies to create a foundational and thorough understanding of how recommender system technology can be used to promote teammate TMMs through information sharing while limiting privacy concerns. Study 1 takes a highly exploratory approach to set a foundation for future dissertation studies. This study investigates what information is perceived to be helpful for promoting teammate TMMs on actual temporary teams. Qualitative data suggests that sharing teammate information related to skills/preferences, conflict management styles, and work ethic/reliability is perceived as beneficial to supporting teammate TMMs. Also, this data provides a foundational understanding for what should be involved in information-sharing recommendations for promoting teammate TMMs. Quantitative results indicate that conflict management data is perceived as more helpful and appropriate to share than personality data. Study 2 investigates the presentation of these recommendations through the factors of anonymity and explanations. Although explanations did not improve trust or satisfaction in the system, providing recommendations associated with a specific teammate name significantly improved several team measures associated with TMMs for actual temporary teams compared to teams who received anonymous recommendations. This study also sheds light on what temporary team members perceive as the benefits to sharing this information and what they perceive as concerns to their privacy. Study 3 investigates how the group/team context and individual differences can influence disclosure behavior when using an information-sharing recommender system. Findings suggest that members of teams who are fully assessed as a team are more willing to unconditionally disclose personal information than members who are assessed as an individual or members who are mixed assessed as an individual and a team. The results also show how different individual differences and different information types are associated with disclosure behavior. Finally, Study 4 investigates how the occurrence and content of explanations can influence disclosure behavior and system perceptions of an information-sharing recommender system. Data from this study highlights how benefit explanations provided during disclosure can increase disclosure and explanations provided during recommendations can influence perceptions of trust competence. Meanwhile, benefit-related explanations can decrease privacy concerns. The aforementioned studies fill numerous research gaps relating to teamwork literature (i.e., TMMs and temporary teams) and recommender system research. In addition to contributions to these fields, this dissertation results in design recommendations that inform both the design of group recommender systems and the novel technology conceptualized through this dissertation, information-sharing recommender systems

Work Behavior Analysis of Indonesian Civil Servants Using Social Media Interactions

This research examined Indonesian civil servants' work behavior using social media interactions. A hermeneutic approach was used to ascertain the meaning of an individual’s actions using text in social media interactions. The results showed that the work behavior of Indonesian civil servants tends to be negative and is primarily due to the unequal distribution of workload. First, this study uses a collection of data obtained from Twitter. Second, the data taken is selected based on the topic of work behavior. This research implies that work behavior needs to be monitored and managed appropriately to enhance public sector organizations' achievement and keep employees in proper conditions. Furthermore, work behavior management can be improved by evenly distributing the workload among employees. Negative work behavior leads to decreased employee performance, which causes dissatisfaction in public services. Work behavior analysis using social media interactions in the public sector is a new theme that needs to be explored in public administration practice. This is a critical topic of research as it can affect the achievements of an organization due to the support of rapid technological developments

Big Data for Qualitative Research

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher

Trolling Twitter

Political polarization is a defining feature of the contemporary American political landscape. While there is little doubt that elite polarization levels have risen dramatically in recent decades, there is some debate over the existence of a corresponding rise in mass polarization. Recent scholarship on mass polarization has cited evidence related to citizens’ positions on public policy issues, party sorting, and geographic polarization; however, questions remain as to the nature and extent of mass polarization in online spaces. Specifically, more needs to be known regarding how expressions of elite polarization influence the formation of polarized communities within social media. This dissertation examines the question: Does elite polarization contribute to mass polarization in social media? This question is approached in three stages. First, this dissertation tests whether or not a causal link between elite and mass polarization strengthens with temporal proximity to highly politicized and potentially polarizing events over the span of the 2016 Republican presidential primary. Second, this dissertation examines the instant effects of elite polarization by examining a minute-by-minute live stream of reactions on Twitter during the first 2016 presidential debate. Third, this dissertation tests a contemporary theory which claims a presidential candidate’s patterns of speech sows the seeds of mass polarization in the form of resentment, fear, or incivility. This dissertation also employs the use of network analysis tools to measure the extent to which polarized communities form on social media in response to elite cues. The nature of such causal relationships provides insight into the influence polarizing messages by elites may have on mass polarization while taking into consideration the unique characteristics of the social media communications environment. In doing so, this dissertation offers a blueprint for future researchers who seek to better understand how networked technologies shape human interactions

Information Producers, Information Consumers : Location Data Privacy in Institutional Settings

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