Latanya Sweeney

Discrimination in online ad delivery

Google ads, black names and white names, racial discrimination, and click advertising.

Putting Health IT on the Path to Success

The promise of health information technology (HIT) is comprehensive electronic patient records when and where needed, leading to improved quality of care at reduced cost. However, physician experience and other available evidence suggest that this promise is largely unfulfilled

Automating Open Science for Big Data

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

Consumer-mediated health information exchanges

The American College of Medical Informatics (ACMI) sponsors periodic debates during the American Medical Informatics Fall Symposium to highlight important informatics issues of broad interest. In 2012, a panel debated the following topic: Resolved: Health Information Exchange Organizations Should Shift Their Principal Focus to Consumer-Mediated Exchange in Order to Facilitate the Rapid Development of Effective, Scalable, and Sustainable Health Information Infrastructure. Those supporting the proposition emphasized the need for consumer-controlled community repositories of electronic health records (health record banks) to address privacy, stakeholder cooperation, scalability, and sustainability. Those opposing the proposition emphasized that the current healthcare environment is so complex that development of consumer control will take time and that even then, consumers may not be able to mediate their information effectively. While privately each discussant recognizes that there are many sides to this complex issue, each followed the debaters tradition of taking an extreme position in order emphasize some of the polarizing aspects in the short time allotted them. In preparing this summary, we sought to convey the substance and spirit of the debate in printed form. Transcripts of the actual debate were edited for clarity, and appropriate supporting citations were added for the further edification of the reader.

DataTags, Data Handling Policy Spaces and the Tags Language

Widespread sharing of scientific datasets holds great promise for new scientific discoveries and great risks for personal privacy. Dataset handling policies play the critical role of balancing privacy risks and scientific value. We propose an extensible, formal, theoretical model for dataset handling policies. We define binary operators for policy composition and for comparing policy strictness, such that propositions like this policy is stricter than that policy can be formally phrased. Using this model, The policies are described in a machine-executable and human-readable way. We further present the Tags programming language and toolset, created especially for working with the proposed model. Tags allows composing interactive, friendly questionnaires which, when given a dataset, can suggest a data handling policy that follows legal and technical guidelines. Currently, creating such a policy is a manual process requiring access to legal and technical experts, which are not always available. We present some of Tags tools, such as interview systems, visualizers, development environment, and questionnaire inspectors. Finally, we discuss methodologies for questionnaire development. Data for this paper include a questionnaire for suggesting a HIPAA compliant data handling policy, and formal description of the set of data tags proposed by the authors in a recent paper.

Practical Differentially Private Modeling of Human Movement Data

Exciting advances in big data analysis suggest that sharing personal information, such as health and location data, among multiple other parties could have significant societal benefits. However, privacy issues often hinder data sharing. Recently, differential privacy emerged as an important tool to preserve privacy while sharing privacy-sensitive data. The basic idea is simple. Differential privacy guarantees that results learned from shared data do not change much based on the inclusion or exclusion of any single person’s data. Despite the promise, existing differential privacy techniques addresses specific utility goals and/or query types (e.g., count queries), so it is not clear whether they can preserve utility for arbitrary types of queries. To better understand possible utility and privacy tradeoffs using differential privacy, we examined uses of human mobility data in a real-world competition. Participants were asked to come up with insightful ideas that leveraged a minimally protected published dataset. An obvious question is whether contest submissions could yield the same results if performed on a dataset protected by differential privacy? To answer this question, we studied synthetic dataset generation models for human mobility data using differential privacy. We discuss utility evaluation and the generality of the models extensively. Finally, we analyzed whether the proposed differential privacy models could be used in practice by examining contest submissions. Our results indicate that most of the competition submissions could be replicated using differentially private data with nearly the same utility and with privacy guarantees. Statistical comparisons with the original dataset demonstrate that differentially private synthetic versions of human mobility data can be widely applicable for data analysis.

An Open Science Platform for the Next Generation of Data

Imagine an online work environment where researchers have direct and immediate access to myriad data sources and tools and data management resources, useful throughout the research lifecycle. This is our vision for the next generation of the Dataverse Network: an Open Science Platform (OSP). For the first time, researchers would be able to seamlessly access and create primary and derived data from a variety of sources: prior research results, public data sets, harvested online data, physical instruments, private data collections, and even data from other standalone repositories. Researchers could recruit research participants and conduct research directly on the OSP, if desired, using readily available tools. Researchers could create private or shared workspaces to house data, access tools, and computation and could publish data directly on the platform or publish elsewhere with persistent, data citations on the OSP. This manuscript describes the details of an Open Science Platform and its construction. Having an Open Science Platform will especially impact the rate of new scientific discoveries and make scientific findings more credible and accountable.