The winners of the 2016 PPPM Award are:
Law Enforcement Access to Data Across Borders: The Evolving Security and Human Rights Issues
by Jennifer Daskal, Associate Professor, American University Washington College of Law
A revolution is underway with respect to law enforcement access to data across borders. Frustrated by delays in accessing sought-after data located across territorial borders, several nations are taking action, often unilaterally, and often in concerning ways. Several nations are considering—or have passed—mandatory data localization requirements, pursuant to which companies doing business in their jurisdiction are required to store certain data, or copies of such data, locally. Such measures facilitate domestic surveillance, increase the cost of doing business, and undercut the growth potential of the Internet by restricting the otherwise free and most efficient movement of data. Meanwhile, a range of nations—including the United Kingdom, Brazil, and others —are asserting that they can unilaterally compel Internet Service Providers (ISPs) that operate in their jurisdiction to produce the emails and other private communications that are stored in other nation’s jurisdictions, without regard to the location or nationality of the target. ISPs are increasingly caught in the middle—being forced to choose between the laws of a nation that seeks production of data and the laws of another nation that prohibits such production. In 2015, for example, Brazilian authorities detained a Microsoft employee for failing to turn over data sought by Brazil; U.S. law prohibited Microsoft from complying with the data request. Governments also are increasingly incentivized to seek other means of accessing otherwise inaccessible data, via, for example, use of malware or other surreptitious forms of surveillance.
While this is a problem of international scope, the United States has an outsized role to play, given a combination of the U.S.-based provider dominance of the market, blocking provisions in U.S. law that prohibit the production of the content of emails and other electronic communications to foreign-based law enforcement, and the particular ways that companies are interpreting and applying their legal obligations. It also means that the United States is uniquely situated to lay the groundwork for an alternative approach that better reflects the normative and practical concerns at stake—and do so in a privacy-protective way. This article analyzes the current state of affairs, highlights the urgent need for a new approach, and suggests a way forward, pursuant to which nations would be able to directly access data from U.S.-based providers when specified procedural and substantive standards are met. The alternative is a Balkanized Internet and a race to the bottom, with every nation unilaterally seeking to access sought-after data, companies increasingly caught between conflicting laws, and privacy rights minimally protected, if at all.
by Joshua A. Kroll, Engineer, Security Team, Cloudflare; Joanna Huey, Princeton University; Solon Barocas, Princeton University; Edward W. Felten, Princeton University; Joel R. Reidenberg, Stanley D. and Nikki Waxberg Chair in Law, Fordham University School of Law; David G. Robinson, Upturn; and Harlan Yu, Upturn
The accountability mechanisms and legal standards that govern such decision processes have not kept pace with technology. The tools currently available to policymakers, legislators, and courts were developed to oversee human decision-makers and often fail when applied to computers instead: for example, how do you judge the intent of a piece of software? Additional approaches are needed to make automated decision systems—with their potentially incorrect, unjustified or unfair results—accountable and governable. This Article reveals a new technological toolkit to verify that automated decisions comply with key standards of legal fairness.
We challenge the dominant position in the legal literature that transparency will solve these problems. Disclosure of source code is often neither necessary (because of alternative techniques from computer science) nor sufficient (because of issues analyzing code) to demonstrate the fairness of a process. Furthermore, transparency may be undesirable, such as when it permits tax cheats or terrorists to game the systems determining audits or security screening, or when it discloses private or protected information.
The central issue is how to assure the interests of citizens, and society as a whole, in making these processes more accountable. This Article argues that technology is creating new opportunities—more subtle and flexible than total transparency—to design decision-making algorithms so that they better align with legal and policy objectives. Doing so will improve not only the current governance of algorithms, but also—in certain cases—the governance of decision-making in general. The implicit (or explicit) biases of human decision-makers can be difficult to find and root out, but we can peer into the “brain” of an algorithm: computational processes and purpose specifications can be declared prior to use and verified afterwards.
The technological tools introduced in this Article apply widely. They can be used in designing decision-making processes from both the private and public sectors, and they can be tailored to verify different characteristics as desired by decision-makers, regulators, or the public. By forcing a more careful consideration of the effects of decision rules, they also engender policy discussions and closer looks at legal standards. As such, these tools have far-reaching implications throughout law and society.
by Danielle Keats Citron, Professor of Law, University of Maryland Carey School of Law
Privacy of Public Data
by Kirsten Martin, Assistant Professor of Strategic Management & Public Policy, George Washington University School of Business; and Helen Nissenbaum, Professor, Media, Culture, and Communication & Computer Science, New York University
The construct of an information dichotomy has played a defining role in regulating privacy: information deemed private or sensitive typically earns high levels of protection, while lower levels of protection are accorded to information deemed public or non-sensitive. Challenging this dichotomy, the theory of contextual integrity associates privacy with complex typologies of information, each connected with respective social contexts. Moreover, it contends that information type is merely one among several variables that shape people’s privacy expectations and underpin privacy’s normative foundations. Other contextual variables include key actors — information subjects, senders, and recipients — as well as the principles under which information is transmitted, such as whether with subjects’ consent, as bought and sold, as required by law, and so forth. Prior work revealed the systematic impact of these other variables on privacy assessments, thereby debunking the defining effects of so-called private information.
In this paper, we shine a light on the opposite effect, challenging conventional assumptions about public information. The paper reports on a series of studies, which probe attitudes and expectations regarding information that has been deemed public. Public records established through the historical practice of federal, state, and local agencies, as a case in point, are afforded little privacy protection, or possibly none at all. Motivated by progressive digitization and creation of online portals through which these records have been made publicly accessible our work underscores the need for more concentrated and nuanced privacy assessments, even more urgent in the face of vigorous open data initiatives, which call on federal, state, and local agencies to provide access to government records in both human and machine readable forms. Within a stream of research suggesting possible guard rails for open data initiatives, our work, guided by the theory of contextual integrity, provides insight into the factors systematically shaping individuals’ expectations and normative judgments concerning appropriate uses of and terms of access to information.
Using a factorial vignette survey, we asked respondents to rate the appropriateness of a series of scenarios in which contextual elements were systematically varied; these elements included the data recipient (e.g. bank, employer, friend,.), the data subject, and the source, or sender, of the information (e.g. individual, government, data broker). Because the object of this study was to highlight the complexity of people’s privacy expectations regarding so-called public information, information types were drawn from data fields frequently held in public government records (e.g. voter registration, marital status, criminal standing, and real property ownership).
Our findings are noteworthy on both theoretical and practical grounds. In the first place, they reinforce key assertions of contextual integrity about the simultaneous relevance to privacy of other factors beyond information types. In the second place, they reveal discordance between truisms that have frequently shaped public policy relevant to privacy. For example,
• Ease of accessibility does not drive judgments of appropriateness. Thus, even when respondents deemed information easy to access (marital status) they nevertheless judged it inappropriate (“Not OK”) to access this information under certain circumstances.
• Even when it is possible to find certain information in public records, respondents cared about the immediate source of that information in judging whether given data flows were appropriate. In particular, no matter that information in question was known to be available in public records, respondents deemed inappropriate all circumstances in which data brokers were the immediate source of information
• Younger respondents (under 35 years old) were more critical of using data brokers and online government records as compared with the null condition of asking data subjects directly, debunking conventional wisdom that “digital natives” are uninterested in privacy.
One immediate application to public policy is in the sphere of access to records that include information about identifiable or reachable individuals. This study has shown that individuals have quite strong normative expectations concerning appropriate access and use of information in public records that do not comport with the maxim, “anything goes.” Furthermore, these expectations are far from idiosyncratic and arbitrary. Our work calls for approaches to providing access that are more judicious than a simple on/off spigot. Complex information ontologies, credentials of key actors (i.e. sender and recipients in relation to data subject), and terms of access – even lightweight ones – such as, identity or role authentication, varying privilege levels, or a commitment to limited purposes may all be used to adjust public access to align better with legitimate privacy expectations. Such expectations should be systematically considered when crafting policies around public records and open data initiatives.
Risk and Anxiety: A Theory of Data Breach Harms
by Daniel Solove, Professor of Law, George Washington University Law School; and Danielle Citron, Professor of Law, University of Maryland Carey School of Law
In lawsuits about data breaches, the issue of harm has confounded courts. Harm is central to whether plaintiffs have standing to sue in federal court and whether plaintiffs have viable claims in tort or contract. Plaintiffs have argued that data breaches create a risk of future injury from identity theft or fraud and that breaches cause them to experience anxiety about this risk. Courts have been reaching wildly inconsistent conclusions on the issue of harm, with most courts dismissing data breach lawsuits for failure to allege harm. A sound and compelling approach to harm has yet to emerge, resulting in a lack of consensus among courts and a rather incoherent jurisprudence.
Two U.S. Supreme Court cases within the past five years have contributed significantly to this tortured state of affairs. In 2013, the Court in Clapper v. Amnesty International concluded that fear and anxiety about surveillance – and the cost of taking measures to protect against it – were too speculative to constitute “injury in fact” for standing. The Court emphasized that injury must be “certainly impending” to be recognized. This past term, the U.S. Supreme Court in Spokeo v. Robins issued an opinion aimed at clarifying the harm required for standing in a case involving personal data. But far from providing guidance, the opinion fostered greater confusion. What the Court made clear, however, was that “intangible” injury, including the “risk” of injury, could be sufficient to establish harm.
Little progress has been made to harmonize this troubled body of law, and there is no coherent theory or approach. In this Article, we examine why courts have struggled when dealing with harms caused by data breaches. We contend that the struggle stems from the fact that data breach harms there are intangible, risk-oriented, and diffuse. Although these characteristics have been challenging to courts in the past, courts have, in fact, been recognizing harms with these characteristics in other areas of law.
We argue that many courts are far too dismissive of certain forms of data breach harm. In many instances, courts should be holding that data breaches cause cognizable harm. We explore why courts struggle to recognize data breach harms and how existing foundations in the law should be used by courts to recognize such harm. We demonstrate how courts can assess risk and anxiety in a concrete and coherent way.
Online Tracking: A 1-million-site Measurement and Analysis
by Steven Englehardt, PhD Candidate, Princeton University
We present the largest and most detailed measurement of online tracking conducted to date, based on a crawl of the top 1 million websites. We make 15 types of measurements on each site, including stateful (cookie-based) and stateless (fingerprinting-based) tracking, the effect of browser privacy tools, and the exchange of tracking data between different sites (“cookie syncing”). Our findings include multiple sophisticated fingerprinting techniques never before measured in the wild. This measurement is made possible by our open-source web privacy measurement tool, OpenWPM1 , which uses an automated version of a full-fledged consumer browser. It supports parallelism for speed and scale, automatic recovery from failures of the underlying browser, and comprehensive browser instrumentation. We demonstrate our platform’s strength in enabling researchers to rapidly detect, quantify, and characterize emerging online tracking behaviors.
The 2016 PPPM Honorable Mentions are:
- Biometric Cyberintelligence, by Professor Margaret Hu, Washington & Lee University School of Law
In this Article, I initiate a project to explore the constitutional and other legal consequences of big data cybersurveillance generally and mass biometric dataveillance in particular. This Article focuses on how biometric data is increasingly incorporated into identity management systems through bureaucratized cybersurveillance or the normalization of cybersurveillance through the daily course of business and integrated forms of governance.
- Ambiguity in Privacy Policies and the Impact of Regulation, by Professors Joel Reidenberg, Fordham University School of Law, Jaspreet Bhatia, Carnegie Mellon University, Travis Breaux, Carnegie Mellon University, and Thomas B. Norton, Fordham University
- Data-Driven Discrimination at Work, by Professor Pauline Kim, Washington University in Saint Louis School of Law
- Friending the Privacy Regulators, by Professor William McGeveran, University of Minnesota Law School
When regulators in different jurisdictions employ this same responsive regulatory strategy, they blur the supposedly sharp distinctions between them, whatever may be written in their respective constitutional proclamations or statute books. Moreover, “regulatory friending” techniques work effectively in the privacy context. Responsive regulation encourages companies to improve their practices continually, it retains flexibility to deal with changing technology, and it discharges oversight duties cost-efficiently, thus improving real-world data practices.