Date: Monday, May 6, 2013
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin 119

Speaker: Lorrie Faith Cranor, Carnegie Mellon University

Title: Necessary But Not Sufficient: Standardized Mechanisms for Privacy Notice and Choice

Abstract: Website privacy policies are supposed to help users make informed decisions about when to share data with websites. But, as anyone who has ever tried to read a privacy policy knows, privacy policies are generally not all that helpful to users. Suggestions are emerging for technical mechanisms that would provide privacy policies in machine-readable form, allowing web browsers, mobile devices, and other tools to act on them automatically and distill them into simple icons for end users. Other proposals are focused on allowing users to signal to websites, through their web browsers, that they do not wish their online activities to be tracked. Industry organizations have brought us web sites where users can opt-out of targeted advertising by their member companies, and a number of software vendors and open source developers are distributing tools that help users block cookies, trackers, or advertising. Facilitating transparency and control through easily recognizable symbols and software privacy controls are laudable goals. However, after more than 15 years of attempts at providing privacy “notice and choice,” we still have a dearth of usable and effective tools that empower consumers to make meaningful privacy choices. In this talk I will review a number of the proposals and tools that have emerged over the years. I’ll talk about the lessons we have learned from these experiences and how they may serve to inform current policy discussions. I’ll also report on some of our research assessing the usability and effectiveness of consumer privacy tools.

Bio: Lorrie Faith Cranor is an Associate Professor of Computer Science and of Engineering and Public Policy at Carnegie Mellon University where she is director of the CyLab Usable Privacy and Security Laboratory (CUPS). She is also a co-founder of Wombat Security Technologies, Inc. During her 2012-13 sabbatical, Lorrie is making art quilts (some on privacy-related themes) as part of her fellowship at the Carnegie Mellon STUDIO for Creative Inquiry. She has authored over 100 research papers on online privacy and usable security. She has played a key role in building the usable privacy and security research community, having co-edited the seminal book Security and Usability (O’Reilly 2005) and founded the Symposium On Usable Privacy and Security (SOUPS). She also chaired the Platform for Privacy Preferences Project (P3P) Specification Working Group at the W3C and authored the book Web Privacy with P3P (O’Reilly 2002). She has served on a number of boards, including the Electronic Frontier Foundation Board of Directors, and on the editorial boards of several journals. In 2003 she was named one of the top 100 innovators 35 or younger by Technology Review magazine. She was previously a researcher at AT&T-Labs Research and taught in the Stern School of Business at New York University. http://lorrie.cranor.org

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Date: Monday, April 22, 2013
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin G125

Speaker: Nicole Immorlica, Microsoft Research in New England

Title: The Degree of Segregation in Social Networks

Abstract: Social networks form the basic medium of social interaction. The structure of these networks significantly impacts and co-evolves with the behavioral patterns of society. Important societal outcomes – the global reach of an epidemic, the degree of cooperation in an online network, the adoption of new technologies – are dictated by social networks. In this talk, we explore the impact of networks on segregation. In 1969, economist Thomas Schelling introduced a landmark model of racial segregation in which individuals move out of neighborhoods where their ethnicity constitutes a minority. Simple simulations of Schelling’s model suggest that this local behavior can cause global segregation effects. In this talk, we provide a rigorous analysis of Schelling’s model on ring networks. Our results show that, in contrast to prior interpretations, the outcome is nearly integrated: the average size of an ethnically-homogenous region is independent of the size of the society and only polynomial in the size of a neighborhood.

Joint work with Christina Brandt, Gautam Kamath, and Robert D. Kleinberg.

Bio: Nicole Immorlica is a researcher at Microsoft Research in New England (MSR NE) and an assistant professor in EECS at Northwestern University. She received her PhD from MIT in 2005 and continued on to do postdocs at Microsoft Research and Centruum voor Wiskunde en Informatica (CWI) before starting her tenure-track job. She is the recipient of various fellowships and awards including the NSF CAREER
Award, the Sloan Fellowship and the Microsoft New Faculty Fellowship. Her research interests lie in the field of algorithmic game theory, specifically social networks, market design, and mechanism design.

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Date: Monday, April 8, 2013
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin G125

Speaker: Brian Levine, University of Massachusetts, Amherst

Title: Fighting Internet-based Sexual Exploitation Crimes Against Children

Abstract: For several years, my research group has been developing strategies to fight online child sexual exploitation, working with a team of sociologists and investigators. Our tools are used daily by law enforcement in the US and other countries, acquiring evidence in many thousands of cases of child pornography. In a portion of the cases, children were rescued. Using data and results from our project, I will provide an analysis of Internet-based child pornography crimes. I’ll illustrate our approach to problems in network-based criminal investigation by presenting summaries of our completed works and works in progress. Our research focus has been on empirical analysis and interdisciplinary collaboration. I’ll discuss the challenges we face and how we’ve addressed them in practice.

I will use our project’s results to support two larger points. First, I will argue for a research agenda in digital forensics that is based on empirical observations of crimes, sociological analysis of the perpetrators, and full understanding of the needs of and restrictions on investigators; such an agenda also advances privacy research, a field I continue to work in. Second, I’ll contrast our work with projects that focus on protecting corporations and individuals from technical flaws in our infrastructure. Even if “fully secured”, the Internet would still play a role aiding and abetting crimes that harm persons directly; interdisciplinary work has a greater chance at addressing problems beyond technical flaws.

Bio: Brian Levine is a Professor in the School of Computer Science at the University of Massachusetts Amherst, which he joined in 1999. He received his PhD in Computer Engineering from the University of California, Santa Cruz in 1999. His research focuses on mobile networks, forensics, privacy, and the Internet. He received an NSF CAREER award in 2002. He was a UMass Lilly Teaching Fellow in 2003 and was awarded his college’s Outstanding Teacher Award in 2007. In 2008, he received the Alumni Award for Excellence in Science & Technology from the Univ. at Albany. In 2011, he was awarded his college’s Outstanding Research Award. He was TPC co-chair of ACM MobiCom 2011, and TPC co-chair of the 2011 and 2012 DFRWS Annual Forensics Research Conferences. This talk is based in part on NSF award CNS-1018615.

Date: Monday, March 25, 2013
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin G125

Speaker: Kenneth D. Mandl, Boston Children’s Hospital | Harvard Medical School
| Harvard-MIT Health Sciences and Technology

Title: Designing the “App Store” for Health

Abstract: Despite the $48 billion dollar Federal investment in health information technology, doctors and hospitals have extraordinary difficulty implementing and using electronic health records (EHRs). How should Medicine construct an intelligent health system that facilitates rapid learning, innovation and transformation in wellness, health care, public health, and research?

Mandl presents the SMART Platform (www.smartplatforms.org), which capacitates EHRs and consumer-facing systems to run substitutable apps. As Mandl described in the New England Journal of Medicine, this software architecture supports creation and sustainability of an extensible ecosystem of apps, and stimulates a market for competition on value and price. The design principle of substitutability produces systems where a physician or researcher or a patient who wishes to add new functionality can simply delete an existing app and download a better one.

Mandl discusses the rapidly approaching tipping point in Medicine’s strategy for managing data and information for health and discovery.
 
Bio: Kenneth D. Mandl, MD, MPH is an Associate Professor at Harvard Medical School (HMS) and the Louis Diamond Investigator at Boston Children’s Hospital, where he directs the Intelligent Health Laboratory within “CHIP”, the Children’s Hospital Informatics Program.

Mandl has pioneered and published extensively in the areas of personal health records and biosurveillance. Under a major a HHS initiative, he co-leads the SMART Platforms project, which seeks to create an “app store” for health. He co-directs a CDC Center of Excellence in Public Health Informatics working to define the role of online social networks in healthcare and public health.

Recognized for his teaching and research, he has received the Barger Award for Excellence in Mentoring at Harvard Medical School and the Presidential Early Career Award for Scientists and Engineers, the highest honor bestowed by the United States government to outstanding scientists and engineers.

He has been an advisor to two Directors of the CDC now chairs the Board of Scientific Counselors of the NIH’s National Library of Medicine.

Dr. Mandl has published over 130 papers in the medical literature and has been elected to multiple honor societies including the American Society for Clinical Investigation, the Society for Pediatric Research, the American College of Medical Informatics and the American Pediatric Society. He leads two postdoctoral training programs in clinical and informatics research and directs the Population Health Track of the new Masters Degree in Biomedical Informatics at HMS. Mandl is a faculty member in the HMS Center for Biomedical Informatics and in the Division of Health Sciences and Technology at Harvard and MIT.

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Date: WEDNESDAY, March 13, 2013
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin G115

Speaker: Jon Kleinberg, Cornell University

Title: Rewards, Badges, and Incentives

Abstract: Many systems involve the allocation of rewards for achievements, and these rewards produce a set of incentives that in turn guide behavior. Such effects are visible in many domains from everyday life, and they are increasingly forming a designed aspect of on-line social media sites through the use of badges and other reward systems. We consider several aspects of the interaction between rewards and incentives, including a framework for reasoning about on-line user behavior in the presence of badges, and some reflections on the incentives produced by the allocation of credit in a simple model of a scientific community.

The talk will be based on joint work with Ashton Anderson, Dan Huttenlocher, Jure Leskovec, and Sigal Oren.

Bio sketch: Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. He is a member of the National Academy of Sciences, National Academy of Engineering, and American Academy of Arts and Sciences; he is the recipient of research fellowships from the MacArthur, Packard, Simons, and Sloan Foundations, as well as awards including the Nevanlinna Prize, the Lanchester Prize, and the ACM-Infosys Foundation Award in the Computing Sciences.

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Date: Monday, March 4, 2013
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin G125

Speaker: Tom Dietterich, Oregon State University

Title: Challenges for Machine Learning in Computational Sustainability

Abstract: Research in computational sustainability seeks to develop and apply methods from computer science to the many challenges of managing the earth’s ecosystems sustainably. Viewed as a control problem, ecosystem management is challenging for two reasons. First, we lack good models of the function and structure of the earth’s ecosystems. Second, it is difficult to compute optimal management policies because ecosystems exhibit complex spatio-temporal interactions at multiple scales.

This talk will discuss some of the many challenges and opportunities for machine learning research in computational sustainability. These include sensor placement, data interpretation, model fitting, computing robust optimal policies, and finally executing those policies successfully. Examples will be discussed on current work and open problems in each of these problems.

All of these sustainability problems involve spatial modeling and optimization, and all of them can be usefully conceived in terms of facilitating or preventing flows along edges in spatial networks. For example, encouraging the recovery of endangered species involves creating a network of suitable habitat and encouraging spread along the edges of the network. Conversely, preventing the spread of diseases, invasive species, and pollutants involves preventing flow along edges of networks. Addressing these problems will require advances in several areas of machine learning and optimization.

Bio: Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces.

Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI. He is President-Elect of AAAI.

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Date: Monday, February 25, 2013
Time: 12:15pm – 1:15pm
Place: Maxwell Dworkin G125

Speaker: Sharon Goldberg, Boston University

Title: The Diffusion of Networking Technologies

Abstract: Two decades of research, engineering, and standardization have resulted in a number of new networking protocols (e.g. IPv6, secure BGP, DNSSEC, etc) that still have not seen widespread adoption on the Internet. Adoption is complicated by the fact that the Internet consists of thousands of interconnected, independent networks that make their own local decisions about whether (or not) to upgrade to a new protocol. In this talk, I consider how the local incentives of these independent networks can be harnessed to drive a cascade that leads to global adoption of a new networking protocol. I will (a) discuss some of the practical issues related to creating incentives for adopting networking protocols like IPv6 and secure BGP, and (b) present a new approximation algorithm, for choosing the smallest set of early adopter networks that can trigger a cascade of adoption.

Based on a tutorial at EC’12, and joint work with Zhenming Liu.

Bio:Sharon Goldberg is an assistant professor of computer science at Boston University. She obtained her PhD from Princeton University in July 2009, and her BASc from the University of Toronto in June 2003. Her research uses tools from theory (cryptography, game theory,
algorithms) and networking (measurement, modeling, and simulation) to solve problems in network security.

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CRCS Lunch Seminar

Date: Monday, February 4, 2013
Time: 12:00pm – 1:00pm
Place: Maxwell Dworkin 119

Speaker:   Salil Vadhan, Harvard SEAS and CRCS

Title:  Privacy Tools for Sharing Research Data

Abstract: I will give an overview of a large, new multidisciplinary project at Harvard on “Privacy Tools for Sharing Research Data.”  The project is a collaborative effort between the Center for Research on Computation and Society, the Institute for Quantitative Social Science, and the Berkman Center for Internet and Society, and is funded as a Frontier grant in the NSF Secure and Trustworthy Cyberspace Program, building on seed funding from Google.

The goal of the project is to help enable the collection, analysis, and sharing of personal data for research in social science and other fields while providing privacy for individual subjects.  Bringing together computer science, social science, statistics, and law, we seek to refine and develop definitions and measures of privacy and data utility, and design an array of technological, legal, and policy tools for social scientists to use when dealing with sensitive data. These tools will be tested and deployed at the Harvard Institute for Quantitative Social Science’s Dataverse Network, an open-source digital repository that offers the largest catalogue of social science datasets in the world. In addition to contributing to research infrastructure for social scientists around the world, the ideas developed in the project may benefit society more broadly as it grapples with data privacy issues in many other domains, including public health and electronic commerce.

Bio: Salil Vadhan is the Vicky Joseph Professor of Computer Science and Applied Mathematics in the Harvard University School of Engineering and Applied Sciences.   His research interests include computational complexity, cryptography, and data privacy.

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Date: Monday, November 26, 2012
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin 119

Speaker:   Felix Wu, Benjamin N. Cardozo School of Law

Title:  Privacy and Utility in Data Sets

Abstract:   Is it possible to publicly release useful data, while preserving the privacy of the individuals whose information is in the database? This question has been the subject of considerable controversy, particularly in the wake of well-publicized instances in which researchers showed how to re-identify individuals in supposedly anonymous data. Some have argued that privacy and utility are fundamentally incompatible, while others have suggested that simple steps can be taken to achieve both simultaneously. Both sides have looked to the computer science literature for support.

What the existing debate has overlooked, however, is that the relationship between privacy and utility depends crucially on what we mean by “privacy” and what we mean by “utility.” Apparently contradictory results in the computer science literature can be explained by the use of different definitions to formalize these concepts. Without sufficient attention to these definitional issues, it is all too easy to over-generalize the technical results. More importantly, formal definitions may never capture some of the nuances in common understandings of “privacy” and “utility,” nuances that are highly contextual and that depend on social factors, not just numbers. By analyzing some of those nuances, we can begin to understand the policy choices inherent in deciding whether and how to regulate data privacy across varying social contexts.

Bio:  Felix Wu is an Associate Professor at the Benjamin N. Cardozo School of Law.  He received his A.B., summa cum laude, in computer science from Harvard University, and both his Ph.D. in computer science and J.D. from the University of California at Berkeley.  After law school, he was an associate first at Covington & Burling in San Francisco and later at Fish & Richardson in Boston, and he clerked for Judge Sandra L. Lynch of the U.S. Court of Appeals for the First Circuit.  His research and teaching focuses on Internet law, information privacy, freedom of speech, and intellectual property.

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Date: Monday, November 19, 2012
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin 119

Speaker:   Arpita Ghosh, Cornell University

Title: Social Computing and User Generated Content: A Game-theoretic Approach

Abstract:  Social computing is now ubiquitous on the Web, with user-generated contributions on sites like Amazon and Yelp, Q&A forums like Y! Answers or StackOverflow, blogs and YouTube forming a growing fraction of the content consumed by Web users. But while there is a large amount of user-generated content online, not all of it is of the same quality. What can we understand, using an economic approach, about what incentive schemes elicit high quality contributions, as well as adequate participation, in such systems?

We provide a game-theoretic model with strategic, attention-motivated contributors within which to address the problem of incentivizing high-quality user-generated content. We first use this model to investigate the widely-used rank-order allocation, where users’ contributions are displayed on a webpage in decreasing order of their ratings: such an allocation of attention constitutes a mechanism, which can influence the quality of content produced by attention-motivated contributors. We show that this rank-order mechanism elicits high quality contributions— in a very strong sense— while also achieving high participation in equilibrium: the lowest quality that can arise in any mixed strategy equilibrium of the rank-order mechanism becomes optimal as the amount of available attention diverges. Additionally, these equilibrium qualities are higher (with probability tending to 1 in the limit of diverging attention) than those elicited in equilibrium by a more equitable proportional mechanism, which distributes attention in proportion to the number of positive ratings a contribution receives. We then move on to crowdsourcing environments with non-diverging rewards, such as the contests hosted by Innocentive or TopCoder, as well as crowdsourced content as in online Q\&A forums, and use a model with endogenous entry to analyze incentivizing high quality in these settings. Unlike models which treat participation as an exogenous choice, the expected number of participants here can be increased by subsidizing entry, potentially improving the expected value of the best contribution. However, we show that free entry is, in fact, dominated by taxing entry— making all entrants pay a small fee which is rebated to the winner can improve the quality of the best contribution over a winner-take-all contest with no taxes.

Based on joint work with Patrick Hummel (EC’11) and Preston McAfee (WWW’11, WWW’12).

Bio: Arpita Ghosh is an Associate Professor of Information Science at Cornell University. Her research focuses on algorithms and mechanism design in the context of strategic behavior on the Web, particularly social computing, user-generated content, and crowdsourcing, and markets and mechanisms for privacy.  She holds a PhD from Stanford University.

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