Join us for CMU Privacy Day 2017 at Carnegie Mellon University. CMU is celebrating the International Data Privacy Day by presenting privacy research and practical advice on protecting privacy online. Privacy Day is open to the public, and no registration is required.
Data Privacy Day is an international effort to empower and educate people to protect their privacy and control their digital footprint. For more information, please visit StaySafeOnline.org
Privacy Day will feature a Privacy Clinic. Come and learn how to protect your privacy. CMU’s information privacy and security students will educate you and answer your questions about privacy risks and remedies concerning many topics, including:
- Web Application for Searching and Comparing Financial Companies' Privacy Practices
- Are you being monitored at Carnegie Mellon?
- Online Tracking and Targeted Ads
- Private Browsing
- The Decline of the Ad Blocker
- Privacy for IoT Devices
- How to Avoid In-App Tracking and Advertising
- Encryption for Messenger Apps
- Opting Out from Ad Targeting
- Analyzing Privacy Requirements for Mobile Apps
- Generating Privacy Policies for Websites and Apps
Refreshments will be provided.
Hosted by the MSIT-Privacy Engineering Program.
Structured probabilistic inference has shown to be useful in modeling complex latent structures of data. One successful way in which this technique has been applied is in the discovery of latent topical structures of text data, which is usually referred to as topic modeling. With the recent popularity of mobile devices and social networking, we can now easily acquire text data attached to meta information, such as geo-spatial coordinates and time stamps. This metadata can provide rich and accurate information that is helpful in answering many research questions related to spatial and temporal reasoning. However, such data must be treated differently from text data. For example, spatial data is usually organized in terms of a two dimensional region while temporal information can exhibit periodicities. While some work existing in the topic modeling community that utilizes some of the meta information, these models largely focused on incorporating metadata into text analysis, rather than providing models that make full use of the joint distribution of meta-information and text.
In this thesis, I propose the event detection problem, which is a multi-dimensional latent clustering problem on spatial, temporal and topical data. I start with a simple parametric model to discover independent events using geo-tagged Twitter data. The model is then improved toward two directions. First, I augmented the model using Recurrent Chinese Restaurant Process (RCRP) to discover events that are dynamic in nature. Second, I studied a model that can detect events using data from multiple media sources. I studied the characteristics of different media in terms of reported event times and linguistic patterns.
The approaches studied in this thesis are largely based on Bayesian non-parametric methods to deal with steaming data and unpredictable number of clusters. The research will not only serve the event detection problem itself but also shed light into a more general structured clustering problem in spatial, temporal and textual data.
Kathleen M. Karley (Chair)
Tom Mitchell (MLD)
Alexander Smola (MLD/Amazon)
Huan Liu (Arizona State University)
In our recent work, we aggressively modify the Java bytecode in order to implement a novel technique called variational execution. As we delve deeply into the bytecode, we realize that bytecode manipulation is a powerful technique that could be applied to various application domains. It has its own unique advantages over similar techniques like source-to-source transformation. It can be useful for simple tasks like performance profiling, refactoring and runtime checking. It is also widely used in research community for more complicated tasks like static analysis and dynamic analysis. In this talk, I am going to briefly introduce Java bytecode and show a few examples of how bytecode manipulation could be useful in a variety of scenarios. My hope is that, after this talk, you have one more implementation option to consider for your research project.
Software crashes is one of the serious category of defects, which are generally dealt with high priority. To debug a software crash, companies such as Microsoft, Apple, Google and Synopsys collect function stack traces. Often the same issue in a code causes crash on different customer sites resulting in submission of multiple crash reports for that issue. Having multiple traces for the same issue could increase turnaround time. Therefore, efficient management of stack traces is required; an approach is required that could group (cluster) the crash reports that are caused by the same issue.
During summer internship at Synopsys Inc., I worked on this problem of clustering of crashes (based on stack traces) that belong to the same issue/component. The problem has already been studied in the past. Most recently Microsoft research (MSR) proposed a solution, which on the surface appeared to solve this problem. However, it turned out to be a number of reasons why it could not be applied directly legacy products such as Synopsys-VCS.
This talks (1) provides an overview of the problem and proposed solution by MSR, (2) discusses the challenges in applying the solution to a legacy product at Synopsys, and (3) reflects upon the lessons learned while working on this project.
Cyber-physical systems (CPSs) are systems that mix software and physical control, with equal prominence. Typically, several software (or cyber) models have been used for the management and control of CPSs. However, to fully realize the goals of CPSs, physical models too have to be treated as first class models in these systems. This gives rise to three main challenges: (a) identifying and integrating physical and software models with different characteristics and semantics; (b) obtaining instances of physical models at a suitable level of abstraction for control; and (c) using and adapting physical models to control CPSs. In this talk, I would discuss these challenges and outline the steps that we have taken to address them in the context of development of power models for a robotic platform named TurtleBot.
The ability to specify immutability in a programming language is a powerful tool for developers, enabling them to better understand and more safely transform their code without fearing unintended changes to program state. The C++ programming language allows developers to specify a form of immutability using the const keyword. In this work, we characterize the meaning of the C++ const qualifier and present the ConstSanitizer tool, which dynamically verifies a stricter form of immutability than that defined in C++: it identifies const uses that are either not consistent with transitive immutability, that write to mutable fields, or that write to formerly-const objects whose const`-ness has been cast away.
We evaluate a set of 7 C++ benchmark programs to find writes-through-const, establish root causes for how they fail to respect our stricter definition of immutability, and assign attributes to each write (namely: synchronized, not visible, buffer/cache, delayed initialization, and incorrect). ConstSanitizer finds 17 archetypes for writes in these programs which do not respect our version of immutability. Over half of these seem unnecessary to us. Our classification and observations of behaviour in practice contribute to the understanding of a widely-used C++ language feature.
Jon Eyolfson is a PhD candidate at the University of Waterloo. His current research is on immutability in the presence of writes. His most recent work involves dynamic empirical analysis of immutability, while continuing work aims to statically analyze immutability. Previously he investigated unread memory using dynamic analysis and an empirical study on what time of the day buggy commits occur.
Faculty Host: Jonathan Aldrich
Despite decades of research into developing abstract security advice and improving interfaces, users still struggle to make passwords. Users frequently create passwords that are predictable for attackers or make other decisions (e.g., reusing the same password across accounts) that harm their security. In this thesis, I use data-driven methods to better understand how users choose passwords and how attackers guess passwords. I then combine these insights into a better password-strength meter that provides real-time, data-driven feedback about the user's candidate password.
I first quantify the impact on password security and usability of showing users different password-strength meters that score passwords using basic heuristics. I find in a 2,931-participant online study that meters that score passwords stringently and present their strength estimates visually lead users to create stronger passwords without significantly impacting password memorability. Second, to better understand how attackers guess passwords, I perform comprehensive experiments on password cracking approaches. I find that simply running these approaches in their default configuration is insufficient, but considering multiple well-configured approaches in parallel can serve as a proxy for guessing by an expert in password forensics. The third and fourth sections of this thesis delve further into how users choose passwords. Through a series of analyses, I pinpoint ways in which users structure semantically significant conteSocietal Computing, Ph.D. Student nt in their passwords. I also examine the relationship between users' perceptions of password security and passwords' actual security, finding that while users often correctly judge the security impact of individual password characteristics, wide variance in their understanding of attackers may lead users to judge predictable passwords as sufficiently strong. Finally, I integrate these insights into an open-source password-strength meter that gives users data-driven feedback about their specific password. I validate this meter through a ten-participant laboratory study and 1,624-participant online study.
Lorrie Faith Cranor (Chair)
Alessandro Acquisti (Heinz)
Lujo Bauer (ECE/ISR)
Jason Hong (HCII)
Michael K. Reiter (University of North Carolina at Chapel Hill)
Developers often build regression test suites that are automatically run to check that code changes do not break any functionality. Nowadays, tests are usually run on a could-based continuous integration service (CIS), e.g., Travis CI. Although regression testing is important, it is also costly, and the cost is reportedly increasing. For example, Google recently reported that they observed quadratic increase in test-suite run time (linear increase in the number of changes and linear increase in the number of tests). One approach to speed up regression testing is regression test selection (RTS), which runs only a subset of tests that may be affected by the latest changes. To detect affected tests, RTS techniques statically analyze the latest changes to a codebase. To obtain overall time saving, compared to rerunning all the tests, RTS techniques have to balance the time spent on analysis vs. the time saved from not running non-selected tests. In addition, novel techniques are needed to reduce other extra costs when running tests on CIS, such as the cost of library retrieval.
I proposed a new, lightweight RTS technique, called Ekstazi, that provides a sweet-spot balancing of the analysis time and time for running non-selected tests. Ekstazi is also the first RTS technique for software that uses modern distributed version-control systems, e.g., Git. I also proposed Molly, a novel technique that substantially reduces the library retrieval cost. Molly lazily retrieves (parts of) libraries only when the libraries are accessed by the language compiler/runtime. I implemented Ekstazi and Molly for Java, and evaluated them on several hundred revisions of 32 open-source projects (totaling 5M lines of code). Ekstazi reduced the overall time 54% compared to running all tests. Furthermore, Molly reduced the library retrieval time by 50%. Finally, only a few months after the initial release, Ekstazi was adopted by several Apache projects.
Milos Gligoric is an Assistant Professor in Electrical and Computer Engineering at the University of Texas at Austin. His research interests are in software engineering and formal methods, especially in designing techniques and tools that improve software quality and developers' productivity. His PhD work has explored test-input generation, test-quality assessment, testing concurrent code, and regression testing. Two of his papers won ACM SIGSOFT Distinguished Paper awards (ICSE 2010 and ISSTA 2015), and three of his papers were invited for journal submissions. Milos was awarded a Google Faculty Research Award (2015) and an NSF CRII Award (2016). Milos’ PhD dissertation won the ACM SIGSOFT Outstanding Doctoral Dissertation Award (2016) and the UIUC David J. Kuck Outstanding Ph.D. Thesis Award (2016). Milos holds a PhD (2015) from UIUC, and an MS (2009) and BS (2007) from the University of Belgrade, Serbia.
Faculty Host: Claire Le Goues
The rise of the Islamic State of Iraq and al-Sham (ISIS) has been watched by millions through the lens of social media. This “crowd” of social media users has given the group broad reach resulting in a massive online support community that is an essential element of their public affairs and resourcing strategies. Other extremist groups have begun to leverage social media as well. Online Extremist Community (OEC) detection holds great potential to enable social media intelligence (SOCMINT) mining as well as informed strategies to disrupt these online communities. I present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and propose several extensions to this methodology to help provide policy makers the ability to identify these communities at scale to understand members’ interests and influence. Ultimately, these methods could provide unique insights to shape information operations, intervention strategies, and policy decisions.
In this thesis, I propose the following contributions:
- efficient search and discovery of OEC members via semi-supervised dense community detection
- an active learning framework to incorporate regional expertise and enable monitoring of highly dynamic OECs
- a formal study of mention strategies used to gain social influence in within a target online community
- an extended literature review of methods applicable to detection, analysis, and disruption of OECs
The contributions proposed in this thesis will be applied to four large Twitter corpora containing distinct online communities of interest. My goal is to provide a substantive foundation enabling follow-on work in this emergent area so critical to counter-terrorism and national security.
Kathleen M. Carley (Chair, ISR)
Daniel Neill (Heinz)
Dr. Zico Kolter (CSD/ISR)
Randy Garrett (IronNet Cybersecurity Inc.)
Social identities, the labels we use to describe ourselves and others, carry with them stereotypes that have significant impacts on our social lives. Our stereotypes, sometimes without us knowing, guide our decisions on whom to talk to and whom to stay away from, whom to befriend and whom to bully, whom to treat with reverence and whom to view with disgust.
Despite the omnipotent impact of identities and stereotypes on our lives, existing methods used to understand them are lacking. In this thesis, I first develop three novel computational tools that further our ability to test and utilize existing social theory on identity and stereotypes. These tools include a method to extract identities from Twitter data, a method to infer affective stereotypes from newspaper data and a method to infer both affective and semantic stereotypes from Twitter data. Case studies using these methods provide insights into Twitter data relevant to the Eric Garner and Michael Brown tragedies and both Twitter and newspaper data from the Arab Spring'.
Results from these case studies motivate the need for not only new methods for existing theory, but new social theory as well. To this end, I develop a new socio-theoretic model of identity labeling - how we choose which label to apply to others in a particular situation. The model combines data, methods and theory from the social sciences and machine learning, providing an important example of the surprisingly rich interconnections between these fields.
Kathleen M. Carley (Chair)
Jason Hong (HCII)
Eric Xing (MLD/LTI,/CSD)
Lynn Smith-Lovin (Duke University)
Robert L. Wilson (Department of Sociology)