Tentatively, there will be no lectures on these dates: January 27; February 3, 8, 15, and 17; March 8, 10, 15, 24, 29, and 31; and April 12 and 14.

Date Speaker Lecture Resources
Fri 11/19 Travis Breaux
  • Title: Legal Compliance, Privacy and Security
  • Abstract: U.S. federal and state regulations impose mandatory and discretionary requirements on industry-wide business practices to achieve non-functional, societal goals such as improved privacy and security. The structure and syntax of regulations affects how well software engineers identify and interpret legal requirements. Inconsistent interpretations can lead to non-compliance and violations of privacy law. To support software engineers who must comply with these regulations, I discuss several research challenges and recent results in the form of validated methods and tools to systematically identify privacy requirements from laws and regulations. These results are motivated by examples from the Health Insurance Portability and Accountability Act of 1996.
  • .pdf
Tue 11/23 Anupam Datta
  • Title: Privacy Policy Specication and Enforcement: Philosophy and Law meets Computer Science
  • .pdf
1, 2

Project: Foundations of Privacy

Mon 12/6 Alessandro Acquisti
  • Title: The Economics and Behavioral Economics of Privacy
  • .pdf
Talks and papers
Tue 1/11 Ramayya Krishnan
  • Bio: Prof. Ramayya Krishnan is Dean of the Heinz College and Director of its iLab. His current interests are in social media analytics and in information privacy and risk management. He can be reached at rk2x@cmu.edu.
  • Title: On Protecting Privacy in Published Network Data
  • Abstract: Social network data is increasingly ubiquitous. Publishing network data while protecting privacy is a challenging problem. Extant approaches attempt to add "noise" to the original network to protect privacy. While they attempt to preserve properties of the original network, extant approaches have not taken the needs of network statistical analysis into account. Using ongoing work on network analysis which does not focus on privacy as a point of departure, I will highlight the problems that arise in conducting network statistical analysis when approaches to protect privacy are applied. The talk will also highlight the availability of large, societal scale network data sets available at the Heinz College iLab and their potential to support research in privacy and in social network analysis.
  • .pdf
Hay, Miklau, and Jensen chapter
Thu 1/13 Jason Hong
  • Title: An Overview for Location Privacy for Mobile Computing
  • .ppt slides
HCI and privacy survey paper
Tue 1/18 Avrim Blum
  • Title: A brief tour of differential privacy
  • pptx, pdf
  • Kobbi Nissim, Sofya Raskhodnikova and Adam Smith: Smooth Sensitivity and Sampling in Private Data Analysis http://www.cse.psu.edu/~asmith/privacy598/papers/nrs08.pdf
  • Authors: Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate: Differentially Private Empirical Risk Minimization http://arxiv.org/abs/0912.0071
  • Avrim Blum, Katrina Ligett and Aaron Roth: A Learning Theory Approach to Non-Interactive Database Privacy http://www.cis.upenn.edu/~aaroth/Papers/dataprivacy.pdf
  • Tue 1/25 Larry Wasserman
    • Larry Wasserman is a Professor in the Department of Statistics and the Machine Learning Department.
    • Title: A Statistical Framework for Differential Privacy
    • Abstract: I'll review differential privacy and discuss how differential privacy affects the accuracy of some statistical procedures. I'll also discuss some shortcomings of differential privacy. (Joint work with Shuheng Zhou.)
    • pdf
    Thu 2/24 Norm Sadeh
    • Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University. His current research interests include Web Security, Privacy and Commerce.
    • Title: User-Controllable Privacy: A Multi-Disciplinary Perspective
    • Abstract: Increasingly users are expected to evaluate and configure a variety of privacy policies (e.g. browser settings, mobile app manifests, or social networking accounts). In practice, research shows that users often have great difficulty evaluating and configuring such policies. As part of this presentation, I will provide an overview of research aimed at empowering users to better control their privacy in the context of a family of location sharing applications we have deployed over the years. This includes technologies to analyze people.s privacy preferences and help design interfaces that are capable of effectively capturing their desired policies. This research helps explain why, with the possible exception of Foursquare, applications in this space have failed to gain traction and what it will likely take to go beyond the mundane scenarios captured by Foursquare. A good part of this talk will be devoted to user-oriented machine learning techniques intended to reduce user-burden and help users converge towards policies they feel more comfortable with. I will also discuss how, beyond just capturing people.s preferences, these same techniques could possibly be used to nudge users towards safer privacy practices.
    • pptx
    Papers: 1,2, 3,4, 5,6, 7
    Thu 3/17 Marco Gruteser
    • Marco Gruteser is an Associate Professor of Electrical and Computer Engineering at Rutgers University. He is a visiting CMU this year, working with Lorrie Cranor.
    • Title: Wireless Location Privacy: Depersonalization Techniques and Connected Vehicle Applications
    • pdf
    Tue 3/29 Steve Fienberg
    • Stephen Fienberg is the Maurice Falk University Professor of Statistics and Social Science in the Department of Statistics, the Machine Learning Department, CyLab, and i-Lab.
    • Title: Statistical Disclosure Limitation and the Challenge of Societal-Scale Data.
    • pdf
    Thu 4/7 Srini Seshan
    • Srini Seshan is an Associate Professor of Computer Science in the Computer Science Department.
    • Title: Improving the Privacy of Wireless Protocols
    • pptx
    Thu 4/21 Bhiksha Raj
    • Bhiksha Raj is an Associate Professor in the Lnaguage Technologies Institute.
    • Title: Privacy issues in speech processing.
    • Abstract: Speech is perhaps one of the most private forms of communication. A person's speech conveys not only what the person says, but also their identity, their emotional state and other such information that the speaker may not want revealed to anyone besides their intended audience. Legally too, the privacy of speech has been recognized: in fact, in many places it is considered illegal to record a person's voice in public even when it is legal to capture their images in video.

      Yet, in spite of the significant theoretical and practical advances in privacy and security technology, little of it has been applied to speech processing.

      In this talk we will present the privacy issues related to various typical voice applications. We will also briefly describe some of our current research directions, and some of the basic tools currently available to develop solutions.

    • ppt
    Tue 4/26 John Lafferty
    • John Lafferty is a Professor of Computer Science, Machine Learning, and Statistics.
    • Title: Compressed Regression
    • Abstract: We present results on a variant of the classical linear regression problem where the original input records are compressed by a random linear transformation. A primary motivation for this compression procedure is to anonymize the data and preserve privacy by revealing only weighted linear combinations of the original observations. We characterize the number of random projections that are required for l1-regularized compressed regression to identify the nonzero coefficients in the true model with probability approaching one. In addition, we show that l1-regularized compressed regression asymptotically predicts as well as an oracle linear model. Finally, we characterize the privacy properties of the compression procedure in information-theoretic terms, establishing upper bounds on the mutual information between the compressed and uncompressed data that decay to zero.

      Joint work with Larry Wasserman (CMU) and Shuheng Zhou (University of Michigan). (Appeared in IEEE Trans. Info. Theory, Vol 55, No. 2, 2009.)

    • pdf

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