Heng Xu - US grants

CT-ER: Privacy Assurance in Location-Based Services:
Integrating Economic Exchange and Social Justice Perspectives

Recent advances in wireless computing and communication have led to the proliferation of location-based services (LBS). While LBS offer users the flexibility of accessing network services on the move, potential privacy violations have emerged as a contentious issue because details of user identities, movements and behaviors are available to LBS providers. Drawing on the economic exchange and social justice theories, this research addresses privacy issues by examining key mechanisms that can alleviate users' privacy concerns. A theoretical framework is developed to link three privacy assurance mechanisms (technology control, industry self-regulation, and government legislation) to the individual privacy decision making process. In addition, as the individual privacy decision making is usually dynamic, context-specific and culture-dependent, two-stage studies are performed to test the research model in three different social contexts and in two countries with different cultures (Singapore and United States).

This research is novel to the extent that existing privacy research has not examined the complex set of inter-related issues in the LBS context. It contributes to a better understanding of the dynamic and dialectic nature of information privacy through a combination of theoretical and empirical research efforts. The findings will have a broader impact in addressing the controversy surrounding the role of technology, industry self-regulation and legislation in bearing the responsibility of assuring individual privacy. Moreover, the interplay between social and technological issues associated with the privacy assurance will be the subject of a number of educational initiatives for Penn State's new major in Security and Risk Analysis.

To better articulate privacy as a dynamic and dialectic phenomenon in a Web 2.0 world, this project proposes a set of basic empirical research activities to investigate three aspects of privacy in online social networks: conceptualization, intervention, and awareness. The goals of this CAREER award are to: 1) improve the theoretical understanding of information privacy in the context of online social networks in an interdisciplinary manner; 2) assess the efficacy of various privacy intervention strategies developed or proposed by the technologists and regulators; and 3) develop and enhance the persuasiveness of privacy and security awareness and training programs. The main contribution of this research is the generation of an interdisciplinary privacy research framework, with extensive grounding in a range of multidisciplinary privacy literatures in behavioral sciences, computer science, information systems, communication, and social psychology. This research takes a holistic view of privacy compared to the large amount of work focusing strictly on data privacy aspects. Thus, it recognizes the critical importance of user perceptions and behaviors when evaluating and developing privacy protection approaches. The proposed three tasks are greatly needed to better connect the social analysis of user privacy behaviors with the technical design of privacy enhancing technologies. The work should contribute to basic science-the underpinnings of user perceptions and actions around privacy management-as well as the findings generated for informing the design of privacy enhancing techniques. The research findings can enable technology-oriented researchers to develop more feasible privacy enhancing techniques that are embedded into the design specifications of systems, as well as aligned with organizational practices and user behaviors. This project will adopt various empirical research methods (experiments, large-scale survey, focus group and social network analysis) to test the behavioral hypotheses underlying user privacy behaviors in the context of online social networks. Well executed surveys and field experiments will contribute substantially to our understanding of how the actual users of online social networks behave and what interventions (in terms of technologies, regulations and trainings) may change their privacy decisions and behaviors.

One of the three major research thrusts is to develop a privacy awareness and training program for users of online social networks. The findings could potentially have a large impact on promoting privacy and security awareness in the user community. The project also has impacts on guideline creation for industry regulators and government agencies. The PI aims to transform the repository of knowledge on the dynamic and dialectic nature of privacy assurance into a number of educational activities in her academic home at Penn State. The educational plan describes the development of new courses and course modules on privacy assurance at both undergraduate and graduate levels, bringing research into classrooms, outreach to students in underrepresented groups, and training of undergraduate and graduate student researchers in interdisciplinary methods. The PI has transformed her science into a privacy workshop for high-school girls and their parents to teach about on-line privacy issues and practices. She proposes to continue such efforts to promote privacy and security awareness among high-school girls, as well as to attract more female students into the program of security and risk analysis at Penn State. The PI will also work with a professional society, the Association for Information Systems, to create a privacy research chapter and a privacy pedagogy section to extend the Information Systems research agenda and curriculum into this important area nationally.

Information privacy and security has long focused on the individual. Most technological safeguards and policies have been oriented towards individual privacy practices (IPP). Yet, many organizational settings are highly collaborative where team work is the norm. Consequently, project researchers will investigate why and how people enact collaborative privacy practices (CPP) and provide design recommendations to develop more effective mechanisms to assure privacy during these activities.
Specifically the project will
1. Improve the conceptual understanding of CPP by investigating these practices in highly collaborative and information-intensive domain where information privacy is essential (e.g., healthcare)
2. Develop a conceptual model of CPP using a multi-method research approach
3. Examine privacy-enhancing technical features that can most effectively support CPP
This project will make three contributions to our knowledge of privacy and security. First, it will advance the theoretical understanding of the collaborative nature of privacy practices. Second, this project will advance the design of privacy enhancing technologies to focus on collaborative privacy practices. Third, it will help foster more effective design interventions by understanding the users? collaborative privacy practices that are often ignored in technical and organizational specifications of privacy and security.
The future development of privacy-enhancing features in information systems must not only focus on privacy assurance for individual users but also privacy assurance during collaborative activities. That is the central thrust of this project. Its broader impact lies in the development of new processes, policies, and technologies to support privacy in collaborative environments without hindering people?s activities in these environments.

The general objective of this research is to address the challenges of protecting adolescent online safety. This work proposes a set of research activities to investigate three aspects of online safety issues for the adolescent cohort: conceptualization, intervention, and education. The specific goals are to: 1) conceptualize information privacy for the adolescent cohort; 2) assess the effectiveness of parental mediation strategies; and 3) generate design recommendations for effective designs of online safety awareness and training programs. The intellectual merit of this work lies in both the strength of the interdisciplinary project team and the significance of the problem addressed. The team includes a broad range of expertise in the domains of information privacy and security, psychology, human-computer interaction, and family and youth resiliency and policy. This work will offer new insights that can address the issues of protecting children online safety as well as study the human-computer and human-human (parent-child) interactions that are aware of the well-being of families.

The findings will have a broader impact in increasing the online safety of adolescents. This work includes significant community outreach and dissemination, involving parents and adolescents from a wide demographic variety in terms of gender, income, ethnicity, and rurality. The proposed intervention approaches will strengthen the trust within parent-child relationship, by enabling parents to show empathy with a child's perspective, and provide choices and options whenever possible. This work will be the first step towards a more ambitious research and intervention project that the team plans to develop in collaboration with national non-profit organizations.

Intelligent, interactive, and highly networked machines -- with which people increasingly interact -- are a growing part of the landscape, particularly in regard to work. As automation today moves from the factory floor to knowledge and service occupations, research is needed to reap the benefits in increased productivity and increased job opportunities, and to mitigate social costs. The workshop supported by this award will promote the convergence of computer science, data management, machine learning, education, and the social and behavioral sciences to define key challenges and research imperatives of the nexus of humans, technology, and work. Convergence is the deep integration of knowledge, theories, methods, and data from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities. This convergence workshop addresses the future of work at the human-technology frontier.

The specific focus of this workshop is on crowdsourcing -- the production of networked knowledge from public participation. This is a new area of research, gaining attention from researchers who study human-computer interactions, data management, machine learning, human behavior, and business. This workshop will bring together researchers from these and other relevant communities to (1) synthesize the diverse perspectives found in these different fields, (2) integrate different knowledge, theories and data to create a transdisciplinary and convergent research roadmap, and (3) catalyze new research directions and advance scientific discovery and innovation in crowdsourcing research. The workshop will also contribute toward broadening participation in this area of research by proactively seeking inclusion of traditionally underrepresented persons.

People today are faced with many privacy decisions in their daily interactions with mobile devices. In the past decade, researchers have studied the design of many tools and mechanisms, such as privacy nudges, that aim to help individuals make better privacy decisions. But just like decision support tools in other domains, these tools cannot make users perfect decision-makers. Users still make mistakes and regret their privacy decisions later. This project casts a fresh perspective on Privacy-by-Redesign by helping users revisit and rectify past privacy decisions that they may regret. In order to avoid annoying users through repetitive alerts, a focus of the project is to identify which past privacy decisions most likely trigger regrets, and to ask users to revisit only those decisions. This project has high societal importance, given that more than 75% of Americans own a smartphone today and need to make frequent privacy decisions. The broader impacts of the project also reach technology developers, policy makers, and consumers by connecting the social analysis of privacy behaviors with the technical design of privacy tools.

This project is rooted in integrating substantive bodies of multidisciplinary knowledge to address the acute challenges of mobile privacy. It develops a theory on how three types of factors, cognitive appraisal, affective states, and environmental cues undercut high-effort decision making and move people toward low-effort information processing, which ultimately leads to regrettable privacy decisions. For the social analysis of privacy behaviors, this project employs a novel combination of experience sampling method and factorial vignette studies to empirically validate the theoretical framework. For the technical design of privacy tools, the project develops an expert-augmented prediction model that infers from data collectible by a mobile operating system the influential factors of cognitive appraisal, affective states, and environmental cues, so as to predict the quality of a privacy decision. The long-term vision of this project is to enable technological designs that help bridge the discrepancies between users' privacy decisions and their perceptions, especially in the context of a mobile system.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Social media data have the potential to track phenomena in real time, such as percentage of the population fearful in the minutes after a disaster or terrorist event, or the degree of anger immediately after the announcement of a jury verdict in a highly publicized case. In each of these examples, it would be difficult to conduct a field survey in real time, and respondents may not be able to reconstruct how they felt or behaved at the time of the event, even if interviewed just a few days later. Social media data have the potential to overcome these limitations. This project will analyze how the application of survey weighting can rebalance samples of Twitter data, and assesses how well this rebalancing will allow valid generalizations about population behaviors. The project will provide a foundation for future advances in the use of social media data for scientific, health, and applied research, thus permitting a wide variety of inferences useful in social policy formulation. A key aspect of the project will provide new evidence regarding the accuracy of migration flows in real time, thus assisting social policy relevant to providing assistance in response to natural disasters.




This project will evaluate the extent to which Twitter users represent or misrepresent the population across different demographic groups and test the feasibility of developing weights that, when applied to Twitter data, make the results more representative of the underlying population. The project conducts the research at the county level in the United States from January 2014-December 2017, using 96% geotagged tweets in the study period and 100% tweets in one month. The project will: (1) extend and refine existing methods for imputing the gender, age, race/ethnicity, and county of residence of each Twitter user; (2) use these values to assess the representativeness of Twitter samples at the county level and explain the determinants of biases; (3) adapt five methods developed for probability or non-probability surveys to reweight Twitter samples and compare their performance in producing model estimates that can be used to infer characteristics of the general population; and (4) test the feasibility of using Twitter data to estimate migration at the county level by comparing to the Internal Revenue Service migration data, as well as estimate Puerto Rico migrants to the continent after Hurricane Maria. Analysis of these migration data will provide a new source of information with which to estimate migration flows in real time and at unprecedentedly detailed geographic scales.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Concerns about a reproducibility crisis in scientific research have become increasingly prevalent within the academic community and to the public at large. The field of meta-science, which performs the scientific study of science itself, is thriving and has examined the existence and prevalence of threats to reproducible and robust research. Most existing replication efforts in social sciences, however, have focused on studies using data from statistically rigorous designed surveys or experiments. Largely missing are replication efforts devoted to examining those studies with organic data, including data organically generated by ubiquitous sensors or mobile applications, twitter feeds, click streams, etc. This project examines the inconsistent handling practices of organic data among scholarly publications in social sciences, in order to establish the confidence (or the lack thereof) in the conclusions drawn from such data analysis. Since findings of social and behavioral sciences inform policy makers on a wide variety of issues, from homeland security to national economy, establishing the confidence of these findings is critical for the proper usage of them, and therefore has broader impacts on all these application areas of national priority.

More specifically, this project starts with determining the extent of, causes of, and remedies for empirical research using organic data that are neither reproducible nor generalizable. The findings from this step raise awareness about the standards and tools for collecting, cleaning, and processing organic data sets across many fields of social sciences. In addition, this project develops new analytical frameworks and methodologies useful for evaluating replicability and robustness of empirical studies with organic data. The vision is for such frameworks to be broadly used in many application domains, thereby fostering cultural change across different fields in social sciences, and bringing the value of reproducibility and robustness to the forefront of data intensive research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.