An extremely belated but enthusiastic welcome Olivia Weng and Jennifer Switzer — two PhD students that joined our group in Fall 2020.
Olivia Weng joins us from the University of Chicago where she got her BS in Computer Science. As an undergraduate, her research with Prof. Andrew Chien (formerly a UCSD professor) studied the use of machine learning techniques to optimize operating system requests.
Jennifer Switzer got an MEng and BS from MIT. Her Masters thesis looked at vulnerabilities that arise when “safe” processes written in Rust interact in potentially unsafe manners through inter-process communication.
Property driven hardware security is a design methodology to assess the safety and security of hardware designs. It enables security experts to describe how the hardware should (or should not) function. These security properties are formally specified using languages that map to models that are easy to verify using existing design tools. There are three fundamental elements for any hardware security design flow. First, security experts need expressive languages to specify these security properties. Second, these properties should map to models to describe the security related behavior of a hardware design. Finally, hardware security design tools verify that the hardware design meets these properties using formal solvers, simulation, and emulation.
The HOST tutorial was one of six selected to provide HOST attendees with an in-depth look at important topics in hardware security. I gave a similar tutorial in the last HOST that was well-received and invited back for another year. This time around, the tutorial included Dr. Nicole Fern from Tortuga Logic. Nicole provided a great presentation on the types of properties that modern hardware security verification tools can handle. I added an in-depth look about how these tools can verify security properties. Have a look yourself at the materials made available to the attendees if you would like.
The Distinguished Lecture was a great honor for me. I really admire the research done in CASA Cluster of Excellence — they have an outstanding group of researchers that I have followed for many years (even decades). This invitation did lead me to consider what one needs to do in order to be eligible to give a distinguished lecture. My conclusion is that one mostly just needs to be a researcher for a long enough time and then their work becomes distinguished. And that made me feel a bit old. So before my talk I made sure to shave and pluck out grey hairs. The folks at CASA did a nice job of producing a video of the talk:
Liver cancer has the fastest growth of incidence and the second highest mortality of all cancers in the United States. Worldwide, it is estimated that over one million people will die from liver cancer in 2030. Liver resection (hepatectomy) is the paradigm for treating liver cancer. A crucial part of a partial hepatectomy is understanding where the tumors, vessels, and other important landmarks are located. To aid in this, the patient typically undergoes preoperative cross-sectional imaging (e.g., CT/MR scans). Surgeons use these images to determine resectability based upon the location of important structures (e.g., veins), analyze tumor margins, accurately compute future liver remnant volumes, and generally aid in surgical planning and navigation.
However, it is challenging for the surgeon to mentally register preoperative cross-sectional images to the surface of the liver at the time of operation since surgical actions cause significant and sometimes permanent liver deformations that lead to mismatches with cross-sectional images. Mentally integrating preoperative data into the operative field is time consuming and error prone. This can make it difficult to accurately localize smaller tumors intra-operatively, which can affect surgical decision making and adequate resection of primary and metastatic liver tumors.
Dr. Michael Barrow‘s PhD thesis developed augmented Reality (AR) image guidance techniques that merge preoperative data directly into the surgeons view during surgery. The goal is to provide surgeons with what Michael describes as “X-ray vision” — allowing them to see through tissues and better understand where blood vessels, tumors, and other important surgical landmarks lie.
The research brings together many state-of-the-art technologies. It requires computer vision approaches to track the surgical scene, real-time mechanical modeling of the organ to accurately place the important unseen surgical landmarks, augmented reality to visualize the landmarks, and hardware accelerated compute systems to process the high throughput sensor data. He showed that patient specific biomechanical modeling results in clinically significant increases in accuracy. Specifically, he built a system that uses magnetic resonance elastography to create a patient-specific mechanical model. The system works in real-time to provide accurate positions of unknown landmarks. He physically validated the techniques by creating a phantom mechanical platform to demonstrate it is possible to track landmarks internal to the phantom liver.
Michael took an unconventional path to his PhD. Unlike most PhDs, he laid out his research topic almost solely on his own. He spent a lot of time shadowing medical doctors to understand their problems. He deftly maneuvered through many different fields, seeking out and finding key collaborators. The result is an amazing example of an interdisciplinary thesis that has tremendous potential value in a clinical setting.
Michael developed a number of other technologies that are not reflected in his thesis. Most recently he is focusing on developing technologies to help into COVID-19 crisis which was awarded an UCSD Institute of Engineering in Medicine Galvanizing Engineering in Medicine award. He lead a team of undergraduates to build systems to better scale the care of COVID-19 patients (for more information see CSE Research Highlight).
Michael was a real tour de force in pushing collaborations between the School of Engineering and the School of Medicine. In addition to his Phd thesis project, he developed a close collaboration with Dr. Shanglei Liu and made many other connections between our research group and the medical school that will certainly create more future fruitful collaborations.
After graduation, Michael started a post-doctoral position at Lawrence Livermore National Labs.
Hardware security-related attacks are growing in number and their severity. Spectre, Meltdown, Foreshadow, Fallout, ZombieLoad, and Starbleed are just a few of the many recent attacks that exploit hardware vulnerabilities. While vulnerabilities are seemingly easy to find, designing secure hardware is challenging (to say the least) and there are limited tools to aid this process.
Armita Ardeshiricham’s PhD thesis made pioneering and fundamental contributions in detecting, localizing, and repairing hardware vulnerabilities. Her thesis developed verification tools that quickly finds vulnerabilities that previous work could not. And it laid the foundation for automated debugging of those flaws.
Her early work focused on developing powerful information flow tracking (IFT) tools that that work at the register transfer level. She extended this work in a fundamentally important manner by formulating IFT logic that detects timing based flows. And she pioneered the idea of sketching for hardware security. The culmination of her PhD research is the VeriSketch framework.
VeriSketch is the first design framework that uses sketching to automatically synthesize secure and functionally-complete hardware design. VeriSketch frees hardware designers from specifying exact cycle-by-cycle behaviors and excruciating bit-level details that often lead to security vulnerabilities. Instead, the designer provides a sketch of the circuit alongside a set of functional and security properties. VeriSketch uses program synthesis techniques to automatically generate a fully-specified design which satisfies these properties. VeriSketch leverages hardware IFT to enable definition and verification of security specifications, which allows for the analysis of a wide variety of security properties related to confidentiality, integrity, and availability.
Armita’s PhD research will undoubtedly have a lasting impact on our group’s hardware security efforts and has laid out a research agenda for the next few years (and likely beyond). Based on her work, we have started projects on error localization (with Prof. Yanjing Li at Univ. of Chicago) and automated property generation (with Prof. Cynthia Sturton at Univ. of North Carolina) that was recently funded by the Semiconductor Research Corporation. Her work was fundamental in developing system on chip access control monitors in collaboration with Leidos and Sant’Anna School of Advanced Studies in Pisa. She will certainly be missed!
Dr. Ardeshiricham currently works at Apple doing things that she can tell no one about (as is typically with Apple). But I’m certain that future Apple devices will be much more secure with her overseeing the verification process.
It is surprisingly easy to extract critical information from a computer chip by simply monitoring the amount of power that it consumes over time. These power side channels have been used time and time again to break otherwise secure cryptographic algorithms. Countless mitigation strategies have been used to thwart these attacks. Their effectiveness is difficult to measure since vulnerability metrics do not adequately consider leakage in a comprehensive manner. In particular, metrics typically focus on single instances in time, i.e., specific attack points, which severely underestimate information leakage especially when considering emerging attacks that target multiple places in the power consumption trace.
We developed a multidimensional metric that addresses these flaws and enables hardware designers to quickly and more effectively understand how the hardware that they develop is resistant to power side channel attacks. Our metric considers all points in time of the power trace, without assuming an underlying model of computation or leakage. This will enable the development of more secure hardware that is resilient to power side channel attacks. This work was recently published at the International Conference on Computer Aided Design (ICCAD), one of the premier forums for technical innovations in electronic design automation.
For further information see: Alric Althoff, Jeremy Blackstone, and Ryan Kastner, “Holistic Power Side-Channel Leakage Assessment: Towards a Robust Multidimensional Metric“, International Conference on Computer Aided Design (ICCAD), November 2019 (pdf)
Deep in the heart of the Peten Basin in Eastern Guatemala lies the ruins of the ancient Maya civilization. Jungles have overtaken these ancient cities, leaving archaeologists to painstakingly excavate their ruins in order to uncover their secrets about their culture, traditions, and rituals. This process is time-consuming and tedious; archaeologists carefully tunnel into the temples and other structures using pickaxes and shovels. They manually sift through the limestone remains in hopes of finding artifacts, tombs, ancient walls, masks, and murals and better understand the usage of these structures and artifacts. The result of this is hundreds of meters of man made tunnels that burrow deep into these structures and snake across multiple levels.
Dr. Quentin Gautier successfully defended his PhD thesis which focused on using modern technologies to better document these archaeological sites. His thesis documents is a series of 3D imaging prototypes, which can generate large-scale 3D models of Maya archaeological sites. Over the years, Quentin lead the development of several generations of scanning systems and he ventured on several expeditions deep in the the Guatemala jungle to deploy these systems. The result is an unprecedented amount of data collection, which has turned into impressive 3D models that are viewable in virtual reality and other 3D visualization systems.
Quentin’s PhD journey was much like these excavations. It was at times painstaking and tedious. He is an expert system builder and this often conflicted with the unfortunate publish-or-perish model of academics. He certainly could have focused on writing more papers on incremental ideas in lieu of developing real systems that were field tested and deployed. In the end, I believe his thesis will be more impactful than these unwritten papers. The excavation sites that he helped document are windows into our past, and many of these windows have been closed as the excavations have been backfilled in order to preserve these precious sites. Quentin’s digital models will allow archaeologists and others all over the world to view these cultural heritage treasures. His system development will help our research group’s continued efforts to use modern technologies to aid in scientific purposes. And his mentorship to the countless undergraduate students (like Giovanni below) will have lasting impacts on their careers.
Congratulations Dr. Gautier and best of luck in Japan! I look forward to seeing all of the amazing systems that you develop in the future.
When Siri, Alexa, Cortana, Google Assistant or your other favorite digital assistant talk to you, they rely on neural networks to create the audio file that speaks to you. WaveNet is a deep neural network for generating audio that provides amazingly accurate results. Yet, this process is slow and cannot be performed in real-time. Our FastWave hardware architecture accelerates this process providing a 10x decrease in the time required to generate the audio file as compared to a state of the art GPU solution. This is the first hardware accelerated platform for autoregressive convolutional neural networks.
FastWave is being presented at the International Conference on Computer-aided Design (ICCAD). ICCAD is one of the top conferences for topics related to hardware design automation. The paper was developed as a project in my CSE 237C class, which teaches hardware design and prototyping using high level synthesis. Shehzeen Hussain, Mojan Javaheripi, and Paarth Neekhara developed the initial idea as a final class project. They continued their work after class and the end result is the paper, FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA.
Dr. Alric Althoff successfully defended his PhD thesis “Statistical Metrics of Hardware Security”, which helps answer a fundamental question: How secure is your hardware? This is a difficult task — defining what it means to be secure is something that the computer security field has grappled with for decades.
There has been a bevy of high profile attacks on hardware most famously Spectre and Meltdown. It is no longer a question of is your hardware secure (that is easy to answer — it is not), but rather how do we know whether a mitigation technique or run-time vulnerability detection mechanism is effective? Alric developed a set of metrics aimed at answering this question. These metrics enable you to rank when your design is most vulnerable to a power side channel attack, answer questions about the randomness of your random number generator, and determine how hardware optimizations and design decisions affect the leakage of secure information.
While we are on the topic of metrics and definitions, I do not yet know how to define “data science” (nor do I think that term will be properly defined for some time), but I do know that Alric is an exemplar of a data scientist. He is able to quickly understand a problem and come up with elegant solutions to those problems. Thus, it is not surprising that Alric has a been a tour de force for our research group playing prominent roles in almost all of our projects. One of my mantras for the past several years has been “You really should talk to Alric about this.”. His thesis is impressive, and yet this is only a small subset of his research during his PhD tenure.
Luckily (for us) Alric is not moving far; he took a position at Leidos just across the street from campus. Hopefully, we can continue to leverage his expertise going forward.
Congrats Dr. Althoff, best of luck in the future, and don’t be a stranger!
Perry deploying a swarm of Autonomous Underwater Explorers.
Dr. Perry Naughton successfully defended his PhD titled “Self-localization of a mobile swarm of underwater vehicles using ambient acoustic noise”. His thesis developed a series of techniques that enabled swarms of underwater vehicles to determine their positions by only listening to the ambient ocean noise.
Underwater localization is an important yet difficult problem since water severely attenuates the GPS signals — it only propagates very short distances (tens of centimeters) and thus we typically rely on active acoustic solutions to localize underwater vehicles. These require extensive infrastructure (e.g., deploying buoys) or are costly (e.g., a Doppler velocity logger costs thousands of USD). Using ambient noise is attractive since it only requires the vehicles to have a microphone which simple and cheap (only tens of USD). Perry’s research showed that it is possible to estimate the geometry of a swarm of mobile, underwater vehicles with ambient acoustic noise.
Doing this work required a large network of collaborators. Perry worked closely with the Jaffe Lab to use their Autonomous Underwater Explorers to validate his ideas. And he spent a year in Grenoble working closely with Philippe Roux on some of the more theoretical aspects of his research. Additionally, he worked with a number of other scientists as part of Engineers for Exploration and CISA3. His “side projects” involved imaging shipwrecks, scanning archaeological sites, and creating large-scale 3D models of coral reefs.
Perry received a large number of fellowships and awards over the years including the NSF Graduate Research Fellowship, NSF Integrative Training and Research Award, NSF Graduate Opportunities World Wide, Chateaubriand STEM Scholarship (French Embassy), Friends of the International Center Scholarship, ARCS Foundation, and the Henry Booker Prize for Ethical Engineering.
Congrats Dr. Naughton! You’ve had an impressive UCSD career over the past decade (Perry was an undergrad here before doing his PhD). You will be missed, but we look forward to seeing the great things that you will do.
We are delighted to welcome Ali Khodamoradi as one of the newest PhD students to the Kastner Research Group (KRG). Ali is far from a stranger. He started working with us in 2012 as volunteer on the Engineers for Exploration camera trap project. About a year after that, he was accepted into the Wireless Embedded Systems MAS program. Last Spring, he graduated from that program, and was accepted into the CSE PhD program. He is the first student to go from WES MAS graduate to CSE PhD program. We are happy to have him as an “official” member, after several years as an “unofficial” member.