Automated Privacy Policy Compliance

Dr. Omar Chowdhury, Purdue University

11:30am Monday, 7 March 2016, ITE325b, UMBC

Privacy regulations often govern data sharing and data use practices of organizations that collect personally identifiable information from their clients. For instance, in the US, healthcare organizations must comply with the federally mandated Health Insurance Portability and Accountability Act (HIPAA). Monetary penalties for non-compliance are high. The current practice of manual auditing for privacy violation is error-prone, cumbersome, and it does not scale well. It is thus crucial for the research community to develop automated tools and techniques to aid organizations in checking privacy policy compliance.

Within this context, I will first present encryption schemes that enable an organization to outsource the storage of audit logs and the computation of compliance checking to an untrusted cloud without completely giving up on privacy. Next, I will present an efficient compliance checker called précis, which leverages techniques from runtime verification and logic programming. Finally, I will conclude with a discussion of some remaining obstacles to practical deployment.

Omar Chowdhury is a Post-Doctoral Research Associate in the Department of Computer Science at Purdue University. Before joining Purdue, he was a Post-Doctoral Research Associate in Cylab at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of Texas at San Antonio. His research interest broadly lies in investigating practically relevant problems of Computer Security and Privacy. His current research focuses on leveraging formal verification and program analysis techniques to check compliance of a system implementation, against well-defined policies and properties. He won the best paper award at the ACM SACMAT’2012. He has also served as a program committee member of ACM SACMAT and ACM CCS.

host: Tim Finin,

Efficient Energy Delivery for Low Power IoT Devices

Khondker Zakir Ahmed, Georgia Institute of Technology

11:00-12:00 Friday, 4 March 2016, ITE325b

Low power IoT Devices are growing in numbers and by 2020 there will be more than 25 Billion of those in areas such as wearables, smart homes, remote surveillance, transportation and industrial systems, including many others. Many IoT electronics either will operate from stand-alone energy supply (e.g., battery) or be self-powered by harvesting from ambient energy sources or have both options. Harvesting sustainable energy from ambient environment plays significant role in extending the operation lifetime of these devices and hence, lower the maintenance cost of the system, which in turn help make them integral to simpler systems. Both for battery-powered and harvesting capable systems, efficient power delivery unit remains an essential component for maximizing energy efficiency.

In this talk, I will discuss some of the most pressing challenges of energy delivery for low power electronics considering both energy harvesting as well as battery-powered conditions. Design techniques for very high conversion ratio, bias current reduction with autonomous bias gating, battery-less cold start, component and power stage multiplexing for reconfigurable and multi-domain regulators will be discussed. I will also present a highly integrated autonomous imaging system featuring a dual-purpose CMOS image sensor that is capable of both imaging and harvesting. This talk will focus only on the energy harvesting and power delivery aspect of this imaging system; presenting ‘a single inductor, single input, four output’power delivery unit with maximum power point tracking and prioritized output voltages. I will also present some silicon results from prototype chips developed in 130nm CMOS.

I will conclude the talk by discussing my vision of research on how low power analog electronics will play significant roles in realizing tomorrow’s ultra-low power, yet highly complex and smart electronic systems.

Khondker Zakir Ahmed is a PhD candidate in the School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, where he works with the supervision of Professor Saibal Mukhopadhyay. He has received his MS in ECE from Georgia Tech in 2015 and BSc in EEE from Bangladesh University of Engineering and Technology in 2004. His primary research focus is power delivery circuit and systems design, specifically focused on low power electronics. His research accomplishments include innovative bias current reduction mechanism (Best in Session award, SRC TECHCON 2014), On-chip controller design for TEG/TEC for joint energy harvesting and hot-spot cooling (Best paper award, ISLPED 2014) and high conversion ratio hybrid down-converting regulator (Best in Session award, SRC TECHCON 2015). Khondker enjoys teaching; he has been a guest lecturer in several courses at Georgia Tech, and was a lecturer in the Department of Electrical and Electronic Engineering at East West University in Dhaka, Bangladesh for over a year before coming to Georgia Tech. Earlier, Khondker has worked as Analog IC Designer from 2005 to 2010 developing commercial power management ICs. He was a graduate intern at Intel Labs in the summers of 2013 and 2014, where he worked on adaptive voltage regulation for guardband reduction and cross-coupled voltage regulators with dynamic load sharing for microprocessors.

CSEE professor Penny Rheingans has been elected to the Computing Research Association (CRA) Board of Directors. She will serve a three year term.

Dr. Rheingans is a Professor of Computer Science and Electrical Engineering and Director of the Center for Women in Technology (CWIT). As CWIT Director, she oversees a scholarship program for undergraduates committed to increasing gender diversity in the technology fields and develops programs to increase the interest and retention of women in technology programs.  She received a Ph.D in Computer Science from the University of North Carolina, Chapel Hill and an AB in Computer Science from Harvard University. Her current research interests include the visualization of predictive models, visualization of data with associated uncertainty, volume rendering, information visualization, perceptual and illustration issues in visualization, non-photorealistic rendering, dynamic and interactive representations and interfaces, and the experimental validation of visualization techniques.

The CRA was founded in 1972 as an association of more than 220 North American academic departments of computer science, computer engineering, and related fields; laboratories and centers in industry, government, and academia engaging in basic computing research; and affiliated professional societies. Its mission is to enhance innovation by joining with industry, government and academia to strengthen research and advanced education in computing. CRA executes this mission by leading the computing research community, informing policymakers and the public, and facilitating the development of strong, diverse talent in the field.

CRA’s Board of Directors is a distinguished group of leaders in computing research drawn from academia and industry. Its members serve on CRA’s standing committees and lead the organization’s responses as new issues affecting computing research arise and evolve.

Spatiotemporal Data Mining and Analytics:
Issues, Methods, and Applications

Shen-Shyang Ho, Nanyang Technological University

12:00pm Thursday, 3 March 2016, ITE325b, UMBC

The extensive and ubiquitous uses of sensors (e.g., satellites, in-situ sensors) and smartphones have resulted in the collection of huge amount of time-stamped data with location information. These large-scale dynamic datasets present many research challenges and application opportunities. In this talk, I describe my research work on spatiotemporal tasks related to (1) application-specific pattern mining, (2) prediction methods, (3) similarity search, and (4) privacy issue. Moreover, I highlight my new research direction in array-based distributed database for spatiotemporal domains.

Shen-Shyang Ho is a tenure-track assistant professor in the School of Computer Engineering at the Nanyang Technological University in Singapore since January 2012. Before this, he was a researcher at the University of Maryland, College Park from 2010 to 2011. He was a postdoctoral scholar at the California Institute of Technology from 2009 to 2010 and a NASA postdoctoral fellow at the Jet Propulsion Laboratory (JPL) from 2007 to 2009. Shen-Shyang received his Ph.D. in Computer Science from George Mason University in 2007 and his Bachelor (Honors) in Science (Mathematics and Computational Science) from the National University of Singapore in 1999. His research was supported by NASA, JPL, and GSFC between 2007 and 2012. His current research is supported by the Ministry of Education (Singapore), National Research Foundation (Singapore), Rolls Royce (UK), and BMW (Germany). He has two US patents and one pending Germany patent. He has given technical tutorials at AAAI (2011), IJCNN (2011), and ECML (2014).

Host: Cynthia Matuszek

Learning models of language, action and perception
for human-robot collaboration

Dr. Stefanie Tellex
Department of Computer Science, Brown University

4:00pm Monday, 7 March 2016, ITE325b

Robots can act as a force multiplier for people, whether a robot assisting an astronaut with a repair on the International Space station, a UAV taking flight over our cities, or an autonomous vehicle driving through our streets.  To achieve complex tasks, it is essential for robots to move beyond merely interacting with people and toward collaboration, so that one person can easily and flexibly work with many autonomous robots.  The aim of my research program is to create autonomous robots that collaborate with people to meet their needs by learning decision-theoretic models for communication, action, and perception.  Communication for collaboration requires models of language that map between sentences and aspects of the external world. My work enables a robot to learn compositional models for word meanings that allow a robot to explicitly reason and communicate about its own uncertainty, increasing the speed and accuracy of human-robot communication.  Action for collaboration requires models that match how people think and talk, because people communicate about all aspects of a robot’s behavior, from low-level motion preferences (e.g., “Please fly up a few feet”) to high-level requests (e.g., “Please inspect the building”).  I am creating new methods for learning how to plan in very large, uncertain state-action spaces by using hierarchical abstraction.  Perception for collaboration requires the robot to detect, localize, and manipulate the objects in its environment that are most important to its human collaborator.  I am creating new methods for autonomously acquiring perceptual models in situ so the robot can perceive the objects most relevant to the human’s goals. My unified decision-theoretic framework supports data-driven training and robust, feedback-driven human-robot collaboration.

Stefanie Tellex is an Assistant Professor of Computer Science and Assistant Professor of Engineering at Brown University.  Her group, the Humans To Robots Lab, creates robots that seamlessly collaborate with people to meet their needs using language, gesture, and probabilistic inference, aiming to empower every person with a collaborative robot.  She completed her Ph.D. at the MIT Media Lab in 2010, where she developed models for the meanings of spatial prepositions and motion verbs.  Her postdoctoral work at MIT CSAIL focused on creating robots that understand natural language.  She has published at SIGIR, HRI, RSS, AAAI, IROS, ICAPs and ICMI, winning Best Student Paper at SIGIR and ICMI, Best Paper at RSS, and an award from the CCC Blue Sky Ideas Initiative.  Her awards include being named one of IEEE Spectrum’s AI’s 10 to Watch in 2013, the Richard B. Salomon Faculty Research Award at Brown University, a DARPA Young Faculty Award in 2015, and a 2016 Sloan Research Fellowship.  Her work has been featured in the press on National Public Radio and MIT Technology Review; she was named one of Wired UK’s Women Who Changed Science In 2015 and listed as one of MIT Technology Review’s Ten Breakthrough Technologies in 2016.

TechRepublic identified CSEE professor Marie desJardins as one of “10 artificial intelligence researchers to follow on Twitter”. Check out her feed at @mariedj17.

“Want to know what’s happening at the epicenter of artificial intelligence? Follow these 10 AI researchers who make the most of their 140 characters on Twitter.”

The Maryland Cyber Challenge, or MDC3, is held in Baltimore each year. The competition, organized by the CyberNEXS division of Leidos, provides a hands-on cybersecurity competition for participants through, among other things, a “king of the hill” style challenge. The event tests not only a team’s ability to break into a target machine, but also its ability to secure that machine from attacks by other competitors. In addition, competitors are given a forensics image of a computer, and tasked with finding hidden flags within the image.

In 2015, about 25 college and university teams qualified for the semi-finals, and eight of those teams made it to the finals.  That eight includes two teams from UMBC: Cyberdawgs 1 and Cyberdawgs 2.  The final round of competition began on October 28, but technical difficulties prevented the contest from finishing.

The contest resumed at noon on Saturday, February 20, 2016.  The Cyberdawgs Team 1,  Tyler Campbell, Josh Domangue, Chris Gardner, Anh Ho, Jacob Rust, and Julio Valcarcel took first place with 18,979 points, outscoring the winning team in the professional division by 5690 points. (No other results or point totals were released.)

The Cyber Defense Team’s meetings are open to all students that have a desire to learn hands-on cybersecurity skills, and no prior cyber experience is needed. The club meets during the Spring 2016 semester on Wednesdays at 7:10PM in ITE 231.  To request access to their mailing list, send an email to . If you have any questions about the club feel free to email Julio Valcarcel () or Anh Ho (). The Cyber Defense Team’s faculty advisers are Dr. Charles Nicholas () and Dr. Rick Forno ().

Learning from High-Dimensional Data via Transformations

Dr. Hossein Mobahi, MIT

12:00pm Monday 29, February 2016, ITE325b

High-dimensional data is ubiquitous in the modern world, arising in images, movies, biomedical measurements, documents, and many other contexts. The “curse of dimensionality” tells us that learning in such regimes is generally intractable. However, practical problems often exhibit special simplifying structures which, when identified and exploited, can render learning in high dimensions tractable. This is a great prospect however, but how to get there in not trivial. In this talk, I will address two challenges associated with high-dimensional learning and discuss my proposed solution.

First, parsimony (sparsity, low rank, etc.) is one of the most prevalent structures in high-dimensional learning applications. However, its presence is often implicit and it reveals itself only after a transformation of the data. Studying the space of such transformations and the associated algorithms for their inference constitute an important class of problems in high-dimensional learning. I will present some of my work in this direction related to image segmentation. I will show how low-rank structures become abundant in images when certain spatial and geometric transformations are considered. This work resulted in a state of the art algorithm for segmentation of natural images.

Second, important scenarios such as deep learning involve high-dimensional nonconvex optimization. Such optimization is generally intractable. However, I show how some properties in the optimization landscape, such as smoothness and stability, can be exploited to transform the objective function to simpler subproblems and allow obtaining reasonable solutions efficiently. The theory is derived by combining the notion of convex envelopes with differential equations. This results in algorithms involving high-dimensional convolution with the Gaussian kernel, which I show has a closed form in many practical scenarios. I will present applications of this work in image alignment, image matching, and deep learning. Furthermore, I will discuss how this theory justifies heuristics currently used in deep learning and suggests new training algorithms that offer a significant speedup.

Hossein Mobahi is a postdoctoral researcher in the Computer Science and Artificial Intelligence Lab at the Massachusetts Institute of Technology. His research interests include machine learning, computer vision, optimization, and especially the intersection of the three. He obtained his PhD from the University of Illinois at Urbana-Champaign in Dec 2012. He is the recipient of Computational Science & Engineering Fellowship, Cognitive Science & AI Award, and Mavis Memorial Scholarship. His recent work on machine learning and optimization have been covered by the MIT news.

The UMBC Cyber Defense Lab presents

Mini-MAC: Raising the Bar for Vehicular Security with a
Lightweight Message Authentication Protocol

Jackson Schmandt, CSEE, UMBC
11:15am-12:30pm Friday, 26 February 2016, ITE 237

We propose Mini-MAC, a new message authentication protocol that works in existing automotive computer networks without delaying any message or increasing network traffic. Deployed in many vehicles, the CAN bus is a low-speed network connecting electronic control units, including those that control critical functionality such as braking and acceleration. The CAN bus is extremely vulnerable to malicious actors with bus access, including wireless access. Traditionally, Message Authentication Codes (MACs) help authenticate the sender of a message, and variants prevent message replay attacks; however, standard MACs are unsuitable for use on the CAN bus because of small payload sizes. Restrictions of the CAN bus, including the need not to delay messages or increase bus traffic, severely limit how well this network can be protected.

Mini-MAC is based on a counter-seeded keyed-Hash MAC (HMAC), augmented with message history and truncated to fit available message space. It does not increase bus traffic and incurs a very small performance penalty relative to the provably secure HMAC. It is the first proposal to combine these two tenets for vehicle networks. The message history feature protects against all transient attackers, even if they know the keys. Though the CAN bus cannot be properly secured against a dedicated attacker, Mini-MAC meaningfully raises the bar of vehicular security, enhancing the safety of drivers and others.

Jackson Schmandt is a MS student in Computer Engineering in the Mobile Pervasive Sensor System Lab. Joint work with Alan Sherman and Nilanjan Banerjee.

Host: Alan T. Sherman,

Establishment of Trust and Integrity in Modern Supply Chains

Ujjwal Guin, University of Connecticut

11:30 Thursday, 25 February 2016, ITE325b

With the advent of globalization and resulting horizontal integration, modern supply chain becomes extremely complex and requires immediate solutions for eliminating counterfeit integrated circuits (ICs), which pose a serious threat to the safety and security of our day-to-day lives. The reliability of such ICs could be questionable as they may have many defects and might not go through as much of a rigorous test process as their authentic counterparts. An adversary can also create a backdoor to bypass the security modules in these ICs. In this research, I have systematically addressed the aforementioned issues by risk analysis and assessment of test methods, and by proposing different Design-for-Anti-Counterfeit (DfAC) measures. As a part of risk analysis, I have developed taxonomies for counterfeit IC types, counterfeit defects, and test methods. Based on these taxonomies, I have introduced novel test metrics and developed a comprehensive framework for assessing a set of test methods to maximize test coverage. In the DfAC domain, I have proposed a suite of solutions to detect counterfeit ICs without performing conventional tests. A set of lightweight negative-bias temperature instability (NBTI)-aware ring oscillators have been developed for combating die and IC recycling. In addition, I have developed a comprehensive solution for preventing intellectual property piracy and IC overproduction by assuring forward trust between all entities involved in the system-on-chip design and fabrication process.

Ujjwal Guin is a PhD candidate at the Electrical and Computer Engineering department of University of Connecticut, where he has been working with Dr. Mark M. Tehranipoor. His current research interests include Hardware Security and Trust, Supply Chain Security, Cybersecurity, and VLSI Design and Test. He has developed several on-chip structures and techniques to improve the security, trustworthiness, and reliability of integrated circuits. He has co-authored a book entitled “Counterfeit Integrated Circuits – Detection and Avoidance”. He has published several journal articles and refereed conference papers. He received Best Student Paper Award from the IEEE North Atlantic Test Workshop (NATW’2013). He is an active participant in the SAE International’s G-19A Test Laboratory Standards Development Committee. Mr. Guin received his B.E. degree from the Department of Electronics and Telecommunication Engineering of Bengal Engineering and Science University, Howrah, India in 2004 and the M.Sc. degree from the Department of Electrical and Computer Engineering of Temple University, Philadelphia, PA, USA in 2010.

Host: Chintan Patel