Engineering Notes on Homomorphic
Private Information Retrieval
Johns Hopkins University Applied Physics Lab
11:15am-12:30pm, Friday, 4 December 4 2015, ITE 231
For two years, we have been investigating applications of private information retrieval (PIR) using the additive homomorphic scheme designed by Paillier that forms the basis of a space efficient PIR system by Ostrovsky, Skeith, and Bethencourt. We have implemented a working prototype and gained some insights about the technique, and identified improvements to make it practical to real-world privacy problems. I will present an overview of the technique, present a real world use case, and discuss our technical contributions and ongoing challenges.
Dr. Russ Fink is Chief Engineer of the Enterprise Security Group at APL. He earned the PhD from UMBC with a dissertation on applying trustworthy computing to voting.
Addressing Energy and Big Data
Challenges in Microgrids
Prof. Ting Zhu, UMBC
1-2pm Friday, Dec 4, 2015, ITE 325B
Buildings account for over 75% of the electricity consumption in the United States. To reduce electricity usage and peak demand, many utility companies are introducing market-based time-of-use (TOU) pricing models. In parallel, government programs that increase the fraction of renewable energy are incentivizing residential consumers to adopt on-site renewables and energy storage. Connecting on-site renewables and energy storage between homes forms a sustainable microgrid that is capable of generating, storing, and sharing electricity to balance local generation and consumption in residential areas. In this talk, I will present two pieces of our work in this area. The first work targets at minimizing the electricity cost from a utility company for a microgrid under different market-based TOU pricing models. This work is selected as the best paper runners up at BuildSys 2014. The goals of the second work are real-time energy data gathering, compression, and recovery based on unique features in the energy consumption patterns. In the end of the talk, I will also briefly introduce some of my latest work in indoor localization, networking, and smart health.
Ting Zhu is an assistant professor in the CSEE at UMBC. He received the Computing Innovation Fellowship in 2010. His papers have been selected in the best paper award finalist in multiple conferences (i.e., SenSys ’10, e-Energy ’13, and BuildSys ’14). He has a broad research interest in areas such as internet of things, energy, networking, systems, big data, and security. He is looking for undergraduate, Master and PhD students to work in the above areas.
Hosts: Professors Fow-Sen Choa () and Alan T. Sherman ()
WalletHub.Com has released its annual survey regarding the states most vulnerable to identity theft.
As a cyber-oriented culture, it’s natural to wonder whether and how a person’s daily habits assist hackers in stealing personal information. Accordingly, the site consulted a panel of cybersecurity professors for answers to such questions and offer advice on how to safeguard data against cybercriminals. Items asked include:
How should consumers choose among third-party providers offering services to protect their identity and personal data?
What can individuals do to guard against identity theft?
Is the recent expansion of social media facilitating identity thefts?
Should the federal government intervene to establish a clear process for victims of identity theft looking to clear their name?
Dr. Rick Forno, CSEE’s Cybersecurity Graduate Program Director and Assistant Director of UMBC’s Center for Cybersecurity, was one of those invited to offer comments on this ongoing problem facing Internet users.
Infrastructure-less Group Data
Sharing using Smart Devices
2:30 Tuesday, 8 December 2015, ITE-346
Advances in pervasive communication technology have enabled many unconventional applications that facilitate and improve the safety and quality of life in modern society. Among emerging applications is situational awareness where individuals and first-responders receive timely alerts about serious events that could have caused the interruption of the services provided by the communication infrastructure such as cellular networks, Wi-Fi hotspots, etc. Another example is when exchanging road conditions between peer-to-peer networked vehicles without the involvement of roadside units. The popularity of smart portable devices such as iPhone and Android powered phones and tablets has made them an attractive choice that can play a role in the realization of these emerging applications. These devices support multiple communication standards and thus enable Device-to-Device (D2D) data exchange at an increased level of convenience. By using technologies such as Bluetooth, Wi-Fi ad-hoc mode and Wi-Fi Direct, these devices are able to communicate without the need for any communication infrastructures. In addition, many of these devices are equipped with sensors that can provide a wealth of information about the surroundings once their readings are aggregated.
However, most existing protocols for data sharing among devices either require an internet connection, which may not be available and may incur extra costs in some cases, or suffer from the device’s operating system limitations. Actually there is no existing solution that allows a set of devices to start sharing data dynamically without forcing the users to apply an elaborate procedure for setting up a group. These shortcomings render existing solutions unsuitable for emergency cases. In this dissertation proposal, we tackle such a problem by developing a framework for enabling data exchange in a cost-effective and timely manner through the establishment of peer-to-peer links among smart devices. In addition, our framework opts to minimize the user required interaction for setting up a connection and overcome the limitations of the operating system.
Our framework consists of a set of protocols for group data exchange using Wi-Fi Direct on Android devices. First we present an Efficient and Lightweight protocol for peer-to-peer Networking of Android smart devices over Wi-Fi Direct (ELN). ELN main goal is to overcome the Wi-Fi Direct support limitations in Android, thus allowing the devices in one Wi-Fi Direct group to communicate together. The ELN protocol is validated by implementing a group chatting application. In addition, we present a protocol for Alert Dissemination using Service discovery (ADS) in Wi-Fi Direct. ADS uses the service discovery feature of Wi-Fi Direct for distributing alerts to nearby devices without requiring any prior connections and thus avoids the setup delay in creating Wi-Fi Direct groups and the limitations of multi-group connectivity in Android. ADS is validated by implementing a hazard propagation application for Android. Finally, we present an Efficient Multi-group formation and Communication (EMC) protocol for Wi-Fi Direct. EMC exploits the battery specifications of the devices to qualify potential group owners and enable dynamic formation of efficient groups. Moreover, EMC allows data exchange between different Wi-Fi direct groups. Part of our implementation of EMC in Android involves the modification of the Android source code to allow multi-group support. A chat application is developed to validate EMC.
To complete the dissertation, we plan to extend EMC by replacing the static assignment of devices’ addresses in our current implementation with an IP address negotiation protocol that runs before creating groups. Such an extension would give greater flexibility in adapting EMC. In addition, we plan to define some criteria for selecting proxy members in order to allow maximum coverage and allow the D2D communication to span a larger geographical area. In addition, we will develop a simulator to do large scale testing for the proposed framework. Finally, we would like to explore the use of dual transceivers in order to increase the robustness of D2D connections when the wireless channels are subject to varying level of interference; particularly we like to investigate the integration of Bluetooth Low Energy within our framework to enable group membership of nodes that do not have Wi-Fi Direct or suffer interference that makes the Wi-Fi Direct links unstable.
Motivated by such learning in nature, the problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One of the approaches to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations.
This work also extends existing work on Bayesian Non-Parametric Feature construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. Performance of the algorithm is shown to be limited by parameters used, implying adjustable capability. A conclusive performance hierarchy between evaluated algorithms is constructed.
Committee: Drs. Tim Oates, Cynthia Matuszek and Tim Finin
Ph.D. Dissertation Defense
Computer Science and Electrical Engineering
University of Maryland, Baltimore County
Rapid Plan Adaptation Through Offline
Analysis of Potential Plan Disruptors
Robert H. Holder, III
9:00am Wednesday, 9 December 2015, ITE 325b
Computing solutions to intractable planning problems is particularly problematic in dynamic, real-time domains. For example, visitation planning problems, such as a delivery truck that must deliver packages to various locations, can be mapped to a Traveling Salesman Problem (TSP). The TSP is an NP-complete problem, requiring planners to use heuristics to find solutions to any significantly large problem instance, and can require a lengthy amount of time. Planners that solve the dynamic variant, the Dynamic Traveling Salesman Problem (DTSP), calculate an efficient route to visit a set of potentially changing locations. When a new location becomes known, DTSP planners typically use heuristics to add the new locations to the previously computed route. Depending on the placement and quantity of these new locations, the efficiency of this adapted, approximated solution can vary significantly. Solving a DTSP in real time thus requires choosing between a TSP planner, which produces a relatively good but slowly generated solution, and a DTSP planner, which produces a less optimal solution relatively quickly.
Instead of quickly generating approximate solutions or slowly generating better solutions at runtime, this dissertation introduces an alternate approach of precomputing a library of high-quality solutions prior to runtime. One could imagine a library containing a high-quality solution for every potential problem instance consisting of potential new locations, but this approach obviously does not scale with increasing problem complexity. Because complex domains preclude creating a comprehensive library, I instead choose a subset of all possible plans to include. Strategic plan selection will ensure that the library contains appropriate plans for future scenarios.
Committee: Drs. Marie desJardins (co-chair), Tim Finin (co-chair), Tim Oates, Donald Miner, R. Scott Cost
CSEE professor Cynthia Matuszek will teach a new special topics course this spring on Principles of Human-Robot Interaction. The graduate level course (CMSC 691-08) will meet on Tuesday and Thursdays from 4:00 to 5:30pm in 013 Sherman Hall.
Principles of Human-Robot Interaction
An introduction to robots in our daily lives
CMSC691-08, 4:00-5:15pm Tue/Thr, starting 26 January 2016, UMBC
Robots are becoming ubiquitous. From Roombas in our homes, to surgical robots in hospitals, to giant manipulators that assemble cars, robots are everywhere. In the past, robots have only ever interacted with highly trained experts. Now, as they are being deployed more widely, we must address new questions about how our robots can interact day-to-day with end users — non-experts — safely, usefully, and pleasantly. This new area of research is called Human-Robot Interaction, or HRI.
This 3-credit special topics course aims to introduce students to current research in HRI and provide hands-on experience with HRI research. Students will explore the diverse range of research topics in this area, learn to identify HRI problems in their own research, and carry out a collaborative project involving human-robot interactions. Topics to be covered include:
Social robots: how can robots be social beings? When do we want them to?
Human-robot collaboration: humans and robots working together on tasks
Natural-language interactions with robots and human-robot dialog
Telerobotics: the uses of remote presence and teleoperation
Expressive robots: how can robots express emotion – and should they?
Students may benefit from having some previous coursework or experience in AI, machine learning, or robotics, but none are necessary. Undergraduate students can enroll with the instructor’s permission. For more information, contact Dr. Matuszek at cmat at umbc.edu.
Is Bigger Better? Comparing User Generated Passwords on
3×3 vs. 4×4 Grid Sizes for Android’s Pattern Unlock
Adam Aviv, USNA
1:00-2:00pm Tuesday, 1 December 2015, ITE 459
Android’s graphical authentication mechanism requires users to unlock their devices by “drawing” a pattern that connects a sequence of contact points arranged in a 3×3 grid. Prior studies have shown that human-generated patterns are far less complex than one would desire; large portions can be trivially guessed with sufficient training. Custom modifications to Android, such as CyanogenMod, offer ways to increase the grid size beyond 3×3, and in this paper we ask the question: Does increasing the grid size increase the security of human-generated patterns?
To answer this question, we conducted two large studies, one in-lab and one online, collecting 934 total 3×3 patterns and 504 4×4 patterns. Analysis shows that for both 3×3 and 4×4 patterns, there is a high incidence of repeated patterns and symmetric pairs (patterns that derive from others based on a sequence of flips and rotations). Further, many of the 4×4 patterns are similar versions of 3×3 patterns distributed over the larger grid space. Leveraging this information, we developed the most advanced guessing algorithm in this space, and we find that guessing the first 20% (0.2) of patterns for both 3×3 and 4×4 can be done as efficiently as guessing a random 2-digit PIN. Guessing larger portions of 4×4 patterns (0.5), however, requires 2-bits more entropy than guessing the same ratio of 3×3 patterns, but the entropy is still on the order of cracking random 3-digit PINs. These results suggest that while there may be some benefit to expanding the grid size to 4×4, the majority of patterns will remain trivially guessable and insecure against broad guessing attacks.
Adam J. Aviv is an Assistant Professor of Computer Science at the United States Naval Academy, receiving his Ph.D. from the University of Pennsylvania under the advisement of Jonathan Smith and Matt Blaze. He has varied research interests including in system and network security, applied cryptography, smartphone security, and more recently in the area of usable security with a focus on mobile devices.
Standing (left to right) are staff: Igor Epshteyn (Coach), CSEE Professor Alan T. Sherman (Director), Joel DeWyer (Business Manager), GM Sam Palatnik (Coach). Sitting (left to right) are the 2015 A Team players: IM Levan “The Georgian Gangster” Bregadze, GM Niclas “The Dark Knight” Huschenbeth (Captain), GM Tanguy “The Belgium Butcher” Ringoir and Dobrynya Konoplev. Photo by Marlayna Demond.
The UMBC chess teams are preparing for the 2015 Pan American Intercollegiate Team Chess Championship which will be hosted by Oberlin College in Cleveland, Ohio on December 27-30. The Pan-Am tournament has been held annually since 1946 and determines the top university chess team in the Americas. UMBC’s chess team has competed in the tournament since 1990 and won or tied for first place ten times, a record only matched by one other college chess team.
UMBC will send a second team to the Pam-Am as well, shown above in a photo by Marlayna Demond. Its members are Nathaniel Wong, Abhilash Puranik, Jeffrey Carr and Mustapha Diomande.
The top four U.S. schools in the 2015 Pan-Am will advance to the President’s Cup, the Final Four of College Chess, which will take place in spring 2016. The Final Four was started in 2001 and determines the top U.S. college team. UMBC is the only school that has qualified to play in all 15 Final Four tournaments and has won a record six times.
Multiple Tenure-track Faculty Positions Starting Fall 2016
Computer Science and Electrical Engineering
University of Maryland, Baltimore County
UMBC’s Department of Computer Science and Electrical Engineering invites applications for three tenure-track Assistant Professor positions to begin in Fall 2016. Exceptionally strong candidates for higher ranks may be considered. Applicants must have or be completing a Ph.D. in a relevant discipline, have demonstrated the ability to pursue a research program, and have a strong commitment to undergraduate and graduate teaching. Candidates will be expected to build and lead a team of student researchers, obtain external research support and teach both graduate and undergraduate courses.
All areas of specialization will be considered, but we are especially interested in candidates in the following areas: information assurance and cybersecurity; mobile, wearable and IoT systems; big data with an emphasis on machine learning, analytics, and high-performance computing; knowledge and database systems; hardware systems and experimental methods in circuits, devices, VLSI, FPGA, and sensors; cyber-physical systems; low-power systems; biomedical and healthcare systems; and methods and tools for hardware-software co-design.
The CSEE department is energetic, research-oriented and multi-disciplinary with programs in Computer Science, Computer Engineering, Electrical Engineering and Cybersecurity. Our faculty (34 tenure-track, six teaching and 15 research) enjoy collaboration, working across our specializations as well as with colleagues from other STEM, humanities and the arts departments and external partners. We have 1500 undergraduate CS and CE majors and 400 M.S. and Ph.D. students in our CS, CE, EE and Cybersecurity graduate programs. We have awarded 276 PhDs since our establishment in 1986. Our research supported by a growing and diverse portfolio from government and industrial sponsors with over $5M in yearly research expenditures. We work to help new colleagues be successful by providing startup packages, reduced teaching loads and active mentoring.
UMBC is a dynamic public research university integrating teaching, research and service. As an Honors University, the campus offers academically talented students a strong undergraduate liberal arts foundation that prepares them for graduate and professional study, entry into the workforce, and community service and leadership. UMBC emphasizes science, engineering, information technology, human services and public policy at the graduate level. We are dedicated to cultural and ethnic diversity, social responsibility and lifelong learning. The 2015 US News and World Report Best Colleges report placed UMBC fourth in the Most Innovative National Universities category and sixth in Best Undergraduate Teaching, National Universities. The Chronicle of Higher Education named UMBC as a Great College to Work For, a recognition given to only 86 universities. Our strategic location in the Baltimore-Washington corridor puts us close to many important federal laboratories and agencies and high-tech companies, facilitating interactions, collaboration, and opportunities for sabbaticals and visiting appointments.
UMBC’s campus is located on 500 acres just off I-95 between Baltimore and Washington DC, and less than 10 minutes from the BWI airport and Amtrak station. The campus includes the bwtech@UMBC research and technology park, which has special programs for startups focused on cybersecurity, clean energy, life sciences and training. We are surrounded by one of the greatest concentrations of commercial, cultural and scientific activity in the nation. Located at the head of the Chesapeake Bay, Baltimore has all the advantages of modern, urban living, including professional sports, major art galleries, theaters and a symphony orchestra. The city’s famous Inner Harbor area is an exciting center for entertainment and commerce. The nation’s capital, Washington, DC, is a great tourist attraction with its historical monuments and museums. Just ten minutes from downtown Baltimore and 30 from the D.C. Beltway, UMBC offers easy access to the region’s resources by car or public transportation.
Applicants should submit a cover letter, a brief statement of teaching and research experience and interests, a CV, and three letters of recommendation at Interfolio. Applications received by January 15, 2016 are assured full consideration and those received later will be evaluated as long as the positions remain open. Send questions to and see the CSEE jobs page for more information.
We are committed to inclusive excellence and innovation and welcome applications from women, minorities, veterans, and individuals with disabilities. UMBC is an affirmative action/equal opportunity employer.