CITP

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This talk is co-sponsored by CITP and the Center for the Study of Democratic Politics.

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This talk will compare two recent advances in the understanding of election integrity.

The Scytl/SwissPost e-voting solution was intended to provide complete verifiability for Swiss government elections. The talk will show failures in both individual verifiability and universal verifiability, based on mistaken implementations of cryptographic components. These failures allow for the construction of “proofs” of an accurate election outcome that pass verification though the votes have been manipulated. This shows that even well-regulated Internet voting systems that seem to provide some evidence of an accurate election outcome are still brittle in their assumptions and incentives, to the extent of making large-scale undetectable fraud relatively easy.  Every time internet voting is reconsidered, we learn about a new aspect that makes it harder than we thought.

On a more optimistic note, the talk will describe a recent pilot of Risk-Limiting Audits of Instant Runoff Votes (IRV) for the San Francisco DA’s race.  Any election might be too close (or any count too error-prone) for confidence to be gained from a small sample, but IRV was once considered too complex and unstable for rigorous audits even when the margin was large. It will be shown that in practice that belief was mistaken: most real IRV elections can be analyzed and reduced to a series of well-defined assertions that can be tested efficiently!

Joint work with Michelle Blom, Andrew Conway, Thomas Haines, Sarah Jamie-Lewis, Olivier Pereira, Philip Stark and Peter Stuckey.

Bio:
Vanessa Teague is the CEO of Thinking Cybersecurity and an associate professor (Adj.) in the Research School of Computer Science at the Australian National University. Her research focuses primarily on cryptographic methods for achieving security and privacy, particularly for issues of public interest such as election integrity and the protection of government data. She was part of the team (with Chris Culnane and Ben Rubinstein) who discovered the easy re-identification of doctors and patients in the Medicare/PBS open dataset released by the Australian Department of Health. She has co-designed numerous protocols for improved election integrity in e-voting systems, and co-discovered serious weaknesses in the cryptography of deployed e-voting systems in New South Wales, Western Australia and Switzerland. She lives and works on Wurundjeri land in Southeastern Australia (near Melbourne).

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To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

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In 2017, Apple announced Safari’s new, on-by-default Intelligent Tracking Prevention feature. It started a revolution among major browser vendors and spawned new web standards initiatives in permissions, ad attribution measurement, storage isolation, and logins. This talk will look at where the web platform is today, where privacy-focused browsers are headed, and what the major challenges are to get to a modern, privacy-preserving web.

Bio: 
John Wilander is a WebKit privacy and security engineer at Apple. He holds a Ph.D. in computer science and has been working in security and privacy since 2001, including six years as chapter leader in the Open Web Application Security Project, OWASP. His most recent work involves three new privacy-preserving web application programming interfaces and the design and implementation of Safari’s Intelligent Tracking Prevention. In his spare time he likes to compose music and write fiction, the latter about hackers of course.

This talk will not be recorded.

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To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

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This talk is co-sponsored by CITP and the University Center for Human Values.

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Al and Bob are very similar, with one specific exception. This exception might pertain to their age, annual salary, health, height, education or sports team preference. Whatever it is, this difference is non-trivial yet also not substantial. Al might be two inches taller, two years younger, earning 5% more money a year, or smoking 5 cigarettes more per day. Al and Bob are subjected to a similar algorithmic decision-making process that generates a score. The algorithmic process assigns scores to Al and Bob that are substantially different from each other. As smaller firms and governmental agencies join larger and more established ones in the process of incorporating machine learning  and other automated processes into their practices, there is reason to believe such scenarios will become common and require close regulatory scrutiny.

Could a small change in inputs justify a substantial change in outputs? Should these scenarios be actively sought out by regulators and auditors, examined with suspicion, counteracted, and perhaps even banned? And, if a data scientist manually, or an algorithm automatically, detects and “smooths” out these types of results, would those corrections introduce problems of their own? Above all, do these situations raise crucial algorithmic fairness concerns that are either overlooked and novel or that are illuminating variations of older ones? Or perhaps such outcomes are perfectly acceptable, and their correction should be avoided.

After first clearly defining “small” and “big” differences in inputs and outputs, this article articulates, formulates and analyzes these questions, which will take us to the bleeding edge of the study of algorithmic decision-making in the fields of computer science and law. The discussion introduces a novel ex-post method to examine algorithmic fairness and efficiency. And yet, at the same time, the article’s analysis will force us to reopen discussions of fairness and equality dating all the way back to Aristotle. The article concludes with policy recommendations to be applied in situations where we will find that the noted dynamics might unfold, and their outcomes will prove unacceptable.

Bio:
Tal Zarsky is a professor of law at the University of Haifa – Faculty of Law. Tal was most recently a visiting scholar and adjunct professor at University of Pennsylvania Carey Law School (2019-2020)). His research focuses on legal theory and allocations, as well as information privacy, algorithmic decisions, cybersecurity, telecommunications and media law, internet policy, and online commerce. He has published numerous articles and book chapters in the U.S. on these matters.  He was a fellow at the Information Society Project at Yale Law School and a Global Hauser Fellow at New York University (NYU) Law School as well as a visiting researcher at the University of Amsterdam and the University of Ottawa. He completed his doctoral dissertation at Columbia University School of Law. He earned a joint bachelor’s degree in law and psychology at the Hebrew University with high honors and his master degree (in law) from Columbia University.

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To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

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Modern software development has embraced the concept of “code reuse,” which is the practice of relying on third-party code to avoid “reinventing the wheel” (and rightly so). While this practice saves developers time and effort, it also creates liabilities: the resulting app may behave in ways that the app developer does not anticipate. This can cause very serious issues for privacy compliance: while an app developer did not write all of the code in their app, they are nonetheless responsible for it. In this talk research that has been conducted to automatically examine the privacy behaviors of mobile apps vis-à-vis their compliance with privacy regulations will be presented.

Using analysis tools that were developed and commercialized (as AppCensus, Inc.), dynamic analysis has been performed on hundreds of thousands of the most popular Android apps to examine what data they access, with whom they share it, and how these practices comport with various privacy regulations, app privacy policies, and platform policies. The research indicates that while potential violations abound, many of the issues appear to be due to the (mis)use of third-party software development kits. An account of the most common types of violations that were observed and ways in which app developers can better identify these issues prior to releasing their apps will be presented.

Bio:
Serge Egelman is the research director of the Usable Security and Privacy group at the International Computer Science Institute (ICSI), which is an independent research institute affiliated with the University of California, Berkeley. He is also CTO and co-founder of AppCensus, Inc., which is a startup that is commercializing his research by performing on-demand privacy analysis of mobile apps for developers, regulators, and watchdog groups. He conducts research to help people make more informed online privacy and security decisions, and is generally interested in consumer protection. This has included improvements to web browser security warnings, authentication on social networking websites, and most recently, privacy on mobile devices. Seven of his research publications have received awards at the ACM CHI conference, which is the top venue for human-computer interaction research; his research on privacy on mobile platforms has received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies and the USENIX Security Distinguished Paper Award, has been cited in numerous lawsuits and regulatory actions, as well as featured in the New York Times, Washington Post, Wall Street Journal, Wired, CNET, NBC, and CBS. He received his Ph.D. from Carnegie Mellon University and has previously performed research at Xerox Parc, Microsoft, and NIST.

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To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

Please join the webinar here.

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Security is too important to leave to chance.  Security by design is often touted as the solution, but when your system is broken before you design it—something has gone very, very wrong.

Secure random number generators are a critical part of most deployed cryptosystems. When they fail, so does the cryptography.

Over the past two decades, researchers have discovered vulnerabilities in many of the most commonly deployed algorithms that generate these random numbers. In more than one instance, researchers discovered flaws in proposed algorithms before it was too late. Yet, these algorithms still went on to become U.S. government standards and were broadly deployed.

This talk draws on Shaanan’s work discovering fatal flaws in real systems to find that behind each one is the hint of a new type of adversary, an adversary who threads flaws into our standards.

Bio:
Shaanan Cohney is a postdoctoral research associate at CITP (2020-2021). Shaanan’s research centers on the interplay between networking protocols and the law, with particular focus on applications of cryptography. His methodology mixes reverse engineering and systems analysis, with approaches from legal scholarship.

Shaanan has won awards for his research and teaching including the Dean’s Award for Excellence in Tutoring (2014), Best Paper at ACM CCS (2016), and the inaugural Geller Fellowship (2019) from the Wharton Public Policy Initiative.

Prior to beginning his role at CITP, Shaanan served as a Cybersecurity Fellow in the office of U.S. Senator Ron Wyden, and as a technologist at the Federal Trade Commission’s Office of Policy Planning.

Shaanan completed his Ph.D., Masters of Science and Engineering, and Masters in Law at the University of Pennsylvania. Prior to that he was awarded a B.Sc and Diploma of Music (Vocal Performance) from the University of Melbourne.

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To request accommodations for a disability, please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

Please join the webinar here.

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Multiple organizations often wish to aggregate their sensitive data and learn from it, but they cannot do so because they cannot share their data. For example, banks wish to run joint anti-money laundering algorithms over their aggregate transaction data because criminals hide their traces across different banks.

To address such problems, Raluca and her students have designed cryptographic protocols and built efficient systems for secure collaborative learning, such as Delphi, Helen, MC^2, and Opaque. This talk will provide an overview of the work in this space, and then focus on one of the systems, Delphi, which enables secure collaborative inference for neural networks.

Bio:
Raluca Ada Popa is an assistant professor of computer science at University of California, Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca received her doctoral degree in computer science as well as her master’s and two bachelors’ degrees from Massachusetts Institute of Technology. She is the recipient of a Sloan Foundation Fellowship award, NSF Career, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT computer science doctoral thesis.

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To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

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Last year CITP launched the tech policy clinic. It is a first-of-its-kind initiative, that bring scholars, students, and practitioners together to solve real-world technology policy problems. This talk will describe how we approach shaping the clinic’s priorities and discuss the challenges and opportunities that lie ahead. Specific examples of our recent work will illustrate how we support evidence-based policies. The importance of flexibility, creativity and the ability to be realistic will be discussed as key components of effective policy interventions.

Bio:
Mihir leads our first-of-its-kind interdisciplinary technology policy clinic that gives students and scholars an opportunity to engage directly in the policy process. Most recently, he served in the New York Attorney General’s Bureau of Internet & Technology as the lead trial counsel in cutting edge matters concerning consumer protection law and technology and obtained one of the largest consumer payouts in the State’s history. Previously, he worked for Cravath, Swaine & Moore LLP and Cahill Gordon Reindel LLP in New York City on a variety of antitrust, securities and commercial disputes involving emerging and traditional industries. Before law school he was a policy analyst at the Electronic Privacy Information Center in Washington, D.C., educating policy makers about the civil liberties implications of new surveillance technologies. Mihir attended Deep Springs College and received an A.B. from Harvard College in 2000 and a law degree from the University of Pennsylvania in 2006.

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To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

Please register here.

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“What can we as voters do to protect our elections and our representative government?”, asks the League of Women Voters of Berkeley Heights, New Providence, and Summit, New Jersey.  Some of the answers include, be aware of election processes in use, and if those are inadequate, press for reforms.  Be aware of election technology in use, and if that is insecure, press for reforms.  Those processes and technology must ensure every citizen has the substantive opportunity to vote, and must count every vote (but just once!).  Participate, as citizens, in running and observing elections.  In the 21st century, new technology has led to new challenges in election security and transparency, but the 2020 pandemic has added special challenges of its own.

Andrew Appel, the Eugene Higgins Professor of Computer Science at Princeton University, will offer his thoughts and insight on the 2020 election process.

The webinar will be live, via Zoom, with an opportunity for questions and discussion.

This event is co-sponsored by the League of Women Voters of Berkeley Heights, New Providence, and Summit, New Jersey.

Please click here to register for the webinar.

This event is open to the public.

Many systems have been proposed for using technology to help individuals and public health officials better respond to the COVID-19 pandemic. This talk will analyze the major proposed uses of information technology in the public health response to COVID-19, including aggregate reporting, contact tracing via direct proximity detection or location history matching, and creation of disease status passports. The public health value of these approaches will be considered along with their privacy and civil liberties implications. For several approaches, broad public acceptance is a prerequisite for success, making careful privacy and civil liberties protection an important contributor to public health goals.

Bio:

Edward W. Felten is the Robert E. Kahn Professor of Computer Science and Public Affairs at Princeton University and the founding director of Princeton’s Center for Information Technology Policy. In 2011-12 he served as the first chief technologist for the Federal Trade Commission. His research interests include computer security and privacy, especially relating to media and consumer products; and technology law and policy. He has published about 80 papers in the research literature and two books. His research on topics such as web security, copyright and copy protection, and electronic voting has been covered extensively in the popular press. His weblog, at freedom-to-tinker.com, is widely read for its commentary on technology, law and policy.

Professor Felten is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, and is a fellow of the ACM. He has testified at House and Senate committee hearings on privacy, electronic voting and digital television. In 2004, Scientific American magazine named him to its list of 50 worldwide science and technology leaders.

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This virtual talk is being co-sponsored by CITP and the Department of Computer Science. It is open only to Princeton University faculty, staff, and students. 

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As our lives become increasingly digital and we produce more and more data, we are witnessing several conflicting trends. On one hand, these massive datasets are becoming more intrusive and privacy-sensitive and on the other they are becoming harder to protect. This is illustrated by the constant occurrence of data breaches in almost every industry and sector.  One of the best tools at our disposal for enforcing data privacy and security is end-to-end encryption where only the data owner hold the encryption key. While end-to end encryption is already deployed in messaging and video calling apps, its widespread adoption is severely limited because it breaks the utility of critical technologies like databases, cloud computing and machine learning.

In this talk, Seny will describe his work designing, analyzing and cryptanalyzing efficient algorithms and systems that operate on end-to-end encrypted data.  He will discuss the advances he and his collaborators have made on these problems over the last fifteen years based on ideas from a variety of fields including cryptography, algorithms, data structures, complexity theory, machine learning and databases. In addition to the technological impact of these advances, societal implications will be discussed which range from (potentially) expanding Law Enforcement’s “Going Dark” problem to enabling new policy trade-offs.

Bio:
Seny Kamara is an associate professor of computer science at Brown University, where he directs the Encrypted Systems Lab and is affiliated with the Center for Human Rights and Humanitarian Studies. Prior to joining Brown, he was a research scientist at Microsoft Research in the Cryptography Research group. At Brown, he conducts research in cryptography with a focus on problems motivated by social and policy issues.

In 2016, he was appointed by the National Academies of Sciences to study the impact of end-to-end encryption on law enforcement and intelligence agencies and in 2019 he testified to the U.S. House of Representative about the privacy and fairness implications of Big Data. He has received a Google Faculty Award and was named a Leadership Fellow by the Boston Global Forum for his work and commitment to global peace.