Bridging Privacy Definitions

Salil Vadhan

Salil Vadhan

Vicky Joseph Professor of Computer Science and Applied Mathematics, SEAS, Harvard
Area Chair for Computer Science

Salil Vadhan is the lead PI of Privacy Tools for Sharing Research Data project and the Vicky Joseph Professor of Computer Science and Applied Mathematics...

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Micah Altman, Alexandra Wood, David R. O'Brien, and Urs Gasser. 2016. “Practical Approaches to Big Data Privacy Over Time.” Brussels Privacy Symposium.Abstract

Increasingly, governments and businesses are collecting, analyzing, and sharing detailed information about individuals over long periods of time. Vast quantities of data from new sources and novel methods for large-scale data analysis promise to yield deeper understanding of human characteristics, behavior, and relationships and advance the state of science, public policy, and innovation. At the same time, the collection and use of fine-grained personal data over time is associated with significant risks to individuals, groups, and society at large. In this article, we examine a range of longterm data collections, conducted by researchers in social science, in order to identify the characteristics of these programs that drive their unique sets of risks and benefits. We also examine the practices that have been established by social scientists to protect the privacy of data subjects in light of the challenges presented in long-term studies. We argue that many uses of big data, across academic, government, and industry settings, have characteristics similar to those of traditional long-term research studies. In this article, we discuss the lessons that can be learned from longstanding data management practices in research and potentially applied in the context of newly emerging data sources and uses.

K. Nissim, A Bembenek, A Wood, M Bun, M Gaboardi, U. Gasser, D O'Brien, T Steinke, and S. Vadhan. 2016. “Bridging the Gap between Computer Science and Legal Approaches to Privacy.” In Privacy Law Scholars Conference. Privacy Law Scholars Conference, Washington D.C., 2016.Abstract
The fields of law and computer science incorporate contrasting notions of the privacy risks associated with the analysis and release of statistical data about individuals and groups of individuals. Emerging concepts from the theoretical computer science literature provide formal mathematical models for quantifying and mitigating privacy risks, where the set of risks they take into account is much broader than the privacy risks contemplated by many privacy laws. An example of such a model is differential privacy, which provides a provable guarantee of privacy against a wide range of potential attacks, including types of attacks currently unknown or unforeseen. The subject of much theoretical investigation, new privacy technologies based on formal models have recently been making significant strides towards practical implementation. For these tools to be used with sensitive personal information, it is important to demonstrate that they satisfy relevant legal requirements for privacy protection. However, making such an argument is challenging due to the conceptual gaps between the legal and technical approaches to defining privacy. Notably, information privacy laws are generally subject to interpretation and some degree of flexibility, which creates uncertainty for the implementation of more formal approaches. This Article articulates the gaps between legal and technical approaches to privacy and presents a methodology for rigorously arguing that a technological method for privacy protection satisfies the requirements of a particular law. The proposed methodology has two main components: (i) extraction of a formal mathematical requirement of privacy based on a legal standard found in an information privacy law, and (ii) construction of a rigorous mathematical proof for establishing that a technological privacy solution satisfies the mathematical requirement derived from the law. To handle ambiguities that can lead to different interpretations of a legal standard, the methodology takes a conservative “worst-case” approach and attempts to extract a mathematical requirement that is robust to potential ambiguities. Under this approach, the mathematical proof demonstrates that a technological method satisfies a broad range of reasonable interpretations of a legal standard. The Article demonstrates the application of the proposed methodology with an example bridging between the requirements of the Family Educational Rights and Privacy Act of 1974 and differential privacy.
Effy Vayena, Urs Gasser, Alexandra Wood, David R. O'Brien, and Micah Altman. 2016. “Elements of a New Ethical Framework for Big Data Research.” Washington and Lee Law Review, 72, 3. Publisher's VersionAbstract

merging large-scale data sources hold tremendous potential for new scientific research into human biology, behaviors, and relationships. At the same time, big data research presents privacy and ethical challenges that the current regulatory framework is ill-suited to address. In light of the immense value of large-scale research data, the central question moving forward is not whether such data should be made available for research, but rather how the benefits can be captured in a way that respects fundamental principles of ethics and privacy.

In response, this Essay outlines elements of a new ethical framework for big data research. It argues that oversight should aim to provide universal coverage of human subjects research, regardless of funding source, across all stages of the information lifecycle. New definitions and standards should be developed based on a modern understanding of privacy science and the expectations of research subjects. In addition, researchers and review boards should be encouraged to incorporate systematic risk-benefit assessments and new procedural and technological solutions from the wide range of interventions that are available. Finally, oversight mechanisms and the safeguards implemented should be tailored to the intended uses, benefits, threats, harms, and vulnerabilities associated with a specific research activity.

Development of a new ethical framework with these elements should be the product of a dynamic multistakeholder process that is designed to capture the latest scientific understanding of privacy, analytical methods, available safeguards, community and social norms, and best practices for research ethics as they evolve over time. Such a framework would support big data utilization and help harness the value of big data in a sustainable and trust-building manner.

Alexandra Wood, Edo Airoldi, Micah Altman, Yves-Alexandre de Montjoye, Urs Gasser, David O'Brien, and Salil Vadhan. 2016. “Comments on the Proposed Rules to Revise the Federal Policy for the Protection of Human Subjects”. Online VersionAbstract

Alexandra Wood, Edo Airoldi, Micah Altman, Yves-Alexandre de Montjoye, Urs Gasser, David O'Brien, and Salil Vadhan submitted comments in response to the September 2015 notice of proposed rulemaking to revise the Federal Policy for the Protection of Human Subjects. With the ability to collect and analyze massive quantities of data related to human characteristics, behaviors, and interactions, researchers are increasingly able to explore phenomena in finer detail and with greater confidence. A major challenge for realizing the full potential of these recent advances will be protecting the privacy of human subjects. Drawing from their research findings and a forthcoming article articulating a modern approach to privacy analysis, the authors offer recommendations for updating the Common Rule to reflect recent developments in the scientific understanding of privacy. The suggested revisions ultimately aim to enable wider collection, use, and sharing of research data while providing stronger privacy protection for human subjects.

 

Specific recommendations include:

 

  • Incorporating clear and consistent definitions for privacy, confidentiality, and security.

  • Providing similar levels of protection to research activities that pose similar risks.

  • Relying on standards and requirements that recognize the limitations of traditional de-identification techniques, the inadequacy of binary conceptions of “identifiable” and “publicly-available” information, and the significance of inference risks to privacy.

  • Creating a new privacy standard based not on a binary identifiability standard, but on the extent to which attributes that may be revealed or inferred depend on an individual’s data and the potential harm that may result.

  • Requiring investigators to conduct systematic privacy analyses and calibrate their use of privacy and security controls to the specific intended uses and privacy risks at every stage of the information lifecycle.

  • Addressing informational risks using a combination of privacy and security controls rather than relying on a single control such as consent or de-­identification and adopting tiered access models where appropriate.

  • Forming an advisory committee of data privacy experts to help the Secretary of Health and Human Services develop guidance on applying privacy and security controls that are closely matched to the intended uses and privacy risks in specific research activities.

 

The authors argue that addressing these issues will help lead researchers towards state-of-the-art privacy practices and advance the exciting research opportunities enabled by new data sources and technologies for collecting, analyzing, and sharing data about individuals.

 
The full comments are also available through Regulations.gov.
Micah Altman, Alexandra Wood, David O'Brien, Salil Vadhan, and Urs Gasser. 2016. “Towards a Modern Approach to Privacy-Aware Government Data Releases.” Berkeley Technology Law Journal, 30, 3.Abstract

This article summarizes research exploring various models by which governments release data to the public and the interventions in place to protect the privacy of individuals in the data. Applying concepts from the recent scientific and legal literature on privacy, the authors propose a framework for a modern privacy analysis and illustrate how governments can use the framework to select appropriate privacy controls that are calibrated to the specific benefits and risks in individual data releases.

David O'Brien, Jonathan Ullman, Micah Altman, Urs Gasser, Michael Bar-Sinai, Kobbi Nissim, Salil Vadhan, Michael Wojcik, and Alexandra Wood. 2015. “Integrating Approaches to Privacy Across the Research Lifecycle: When is Information Purely Public?.” Social Science Research Network. SSRN VersionAbstract

On September 24-25, 2013, the Privacy Tools for Sharing Research Data project at Harvard University held a workshop titled "Integrating Approaches to Privacy across the Research Data Lifecycle." Over forty leading experts in computer science, statistics, law, policy, and social science research convened to discuss the state of the art in data privacy research. The resulting conversations centered on the emerging tools and approaches from the participants’ various disciplines and how they should be integrated in the context of real-world use cases that involve the management of confidential research data.

Researchers are increasingly obtaining data from social networking websites, publicly-placed sensors, government records and other public sources. Much of this information appears public, at least to first impressions, and it is capable of being used in research for a wide variety of purposes with seemingly minimal legal restrictions. The insights about human behaviors we may gain from research that uses this data are promising. However, members of the research community are questioning the ethics of these practices, and at the heart of the matter are some difficult questions about the boundaries between public and private information. This workshop report, the second in a series, identifies selected questions and explores issues around the meaning of “public” in the context of using data about individuals for research purposes.

Micah Altman, David O’Brien, Salil Vadhan, and Alexandra Wood. 2014. “Comment to The White House Office of Science and Technology Policy (OSTP): Big Data Study, Request for Information”.Abstract
On January 23, 2014, President Barack Obama asked John Podesta to perform a comprehensive review of big data and privacy. During this review, the White House Office of Science and Technology Policy issued a request for public comment on questions related to the public policy implications of big data.
 
 
Micah Altman, David O’Brien, Salil Vadhan, and Alexandra Wood submitted a response on behalf of the Privacy Tools for Sharing Research Data project. Their comments outline a broad, comprehensive, and systematic framework for privacy analysis and provide a taxonomy of modern technological, statistical, and cryptographic approaches to preserving both data privacy and utility. They argue that an analysis of information privacy should address the scope of information covered, the sensitivity of that information, the risk that sensitive information will be disclosed, the availability of control and accountability mechanisms, and the suitability of existing data sharing models, as applied across the entire lifecyle of information use, from collection through dissemination and reuse.
 
 
With this submission, the authors discuss the inadequacy of traditional approaches to privacy protection and recommend a modern approach that considers three principles. First, the risks of informational harm are generally not a simple function of the presence or absence of specific fields, attributes, or keywords in the released set of data. Second, redaction, pseudonymization, coarsening, and hashing, are often neither an adequate nor appropriate practice, nor is releasing less information necessary more privacy protective. Third, a thoughtful analysis with expert consultation is necessary in order to evaluate the sensitivity of the data collected, to quantify the associated re-identification risks, and to design useful and safe release mechanisms.
Alexandra Wood

Alexandra Wood

Research Fellow, Berkman Center for Internet & Society, Harvard
Senior Researcher, Privacy Tools Project

Alexandra Wood is a research fellow at the Berkman Center for Internet & Society and a member of the Privacy Tools law and policy team. Her research...

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Kobbi Nissim

Kobbi Nissim

Senior Research Fellow at Harvard University
Professor of Computer Science at Georgetown University
Micah Altman

Micah Altman

Director of Research and Head/Scientist, Program on Information Science for the MIT Libraries, MIT
Non-Resident Senior Fellow, The Brookings Institution
Current Member of Datatags Team