Publications

2017
Bar-Sinai M, Medzini R. Public Policy Modeling using the DataTags Toolset. 2017.Abstract

We apply Tags, a framework for modeling data handling policies, to a welfare policy - the chapter in the Israel National Insurance law dealing with unemployment benefits. The generated model is useful for assessing entitlements of specific cases, and for gaining insights into the modeled policy as a whole.

This is a proof-of-concept poster, presented at ESPAnet Israel, 2017.

The sample server can be access at: http://tinyurl.com/inii-tags

 

Decision Graph.pdf Tag Space.pdf Poster.pdf
2016
Bar-Sinai M, Sweeney L, Crosas M. DataTags, Data Handling Policy Spaces and the Tags Language, in In Proceedings of the International Workshop on Privacy Engineering, IEEE. San-Jose, CA, USA: IEEE ; 2016. Publisher's VersionAbstract
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.
2015
Crosas M, King G, Honaker J, Sweeney L. Automating Open Science for Big Data. The ANNALS of the American Academy of Political and Social Science [Internet]. 2015;659 (1) :260-273. Publisher's VersionAbstract

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.

Sweeney L. All the Data on All the People, in The Privacy Law Scholars Conference (PLSC). Berkeley, California: UC Berkeley Law School & GWU Law School (Berkeley Center for Law & Technology) ; 2015.
Sweeney L, Crosas M. An Open Science Platform for the Next Generation of Data. Arxiv.org Computer Science, Computers and Scoiety [Internet] [Internet]. 2015. ArXiv's VersionAbstract

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.

sweeneycrosas1.pdf
Sweeney L, Crosas M, Bar-Sinai M. Sharing Sensitive Data with Confidence: The Datatags System. Technology Science [Internet]. 2015. Technology ScienceAbstract

Society generates data on a scale previously unimagined. Wide sharing of these data promises to improve personal health, lower healthcare costs, and provide a better quality of life. There is a tendency to want to share data freely. However, these same data often include sensitive information about people that could cause serious harms if shared widely. A multitude of regulations, laws and best practices protect data that contain sensitive personal information. Government agencies, research labs, and corporations that share data, as well as review boards and privacy officers making data sharing decisions, are vigilant but uncertain. This uncertainty creates a tendency not to share data at all. Some data are more harmful than other data; sharing should not be an all-or-nothing choice. How do we share data in ways that ensure access is commensurate with risks of harm?