Division of Computer and Network Systems (CNS)
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Submitted by Dmitry Evtyushkin on Tue, 03/19/2019 - 10:22am
Branch predictor (BP) is one of the key performance improvement mechanisms in today's processors. Recent studies demonstrate that it can be used to initiate powerful attacks such as side-channel and speculative execution-based attacks. These attacks allow adversaries to steal sensitive data and compromise computer systems. This project investigates security threats introduced by existing BP designs and develops new safe designs to stop BP-related attacks without significantly degrading the performance.
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Submitted by Sagar Samtani on Tue, 03/19/2019 - 10:04am
Hackers often target the information systems that underlie critical systems in domains ranging from finance to healthcare. The estimated cost of defending against and responding to hacking incidents currently runs at hundreds of billions of dollars annually. To reduce these costs, many organizations have aimed to develop timely, relevant, actionable, and shareable Cyber Threat Intelligence (CTI) about security and privacy threats to support cybersecurity decision-making. However, existing methods tend to react to known threats rather than proactively detecting emerging ones.
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Submitted by aanjhan on Tue, 03/19/2019 - 10:00am
Modern localization systems such as the Global Positioning System have widely demonstrated vulnerabilities to signal-spoofing and jamming attacks. With the advent of autonomous cyber-physical systems such as self-driving cars and unmanned aerial vehicles, the ability to securely estimate, track and verify one's location is increasingly critical, indicative of a strong need to realize localization systems that are resilient to modern day cyber-physical attacks.
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Submitted by Md Rahman on Tue, 03/19/2019 - 9:57am
Rowhammer is a software-assisted cyber attack that causes malicious changes to the target memory cells of dynamic random-access memory (DRAM) due to charge leakage, by crafting memory access patterns which rapidly access the same row multiple times. This research focuses on proper hardware characterization towards a Rowhammer-resistant memory system. This characterization will also inform whether Rowhammer susceptibility increases with aging, and if so, will enable a method for detecting recycled chips.
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Submitted by Rachel Cummings on Tue, 03/19/2019 - 9:53am
This project lays the groundwork for understanding how existing tools for privacy-preserving data analysis interact with strategic and human aspects of practical privacy guarantees. When strategic individuals have privacy concerns about the use of their data, they may modify their behavior to ensure less, or perhaps more favorable, information is revealed. The project's novelties are an interdisciplinary approach, which combines tools from algorithm design, machine learning, and economics.
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Submitted by Hai Phan on Tue, 03/19/2019 - 9:51am
The rapid development of machine learning in the domain of healthcare presents clear privacy issues, when deep neural networks and other models are built based on patients' personal and highly sensitive data such as clinical records or tracked health data. Further, these models can be vulnerable to attackers trying to infer the sensitive data that was used to build the model.
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Submitted by Jake Williams on Mon, 03/18/2019 - 3:43pm
This project aims to develop and deploy an information veracity evaluation system to support online discourse moderation and human comprehension of online information. Understanding information's nature can help users to identify essential products and services, and even potentially help to inform democratic participation.
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Submitted by Anupam Das on Mon, 03/18/2019 - 3:39pm
With the rapid adoption of the Internet of Things (IoT), we face a new world, where we are never alone. At all times, a plethora of connected devices, from smartphones to home assistants to motion detectors continuously sense and monitor our activities. While these devices provide us convenience, they are often backed by powerful analytics to sift through large volume of personal data, at times collected without our awareness or consent.
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Submitted by Bradley Reaves on Mon, 03/18/2019 - 3:26pm
Automated calls (often called "robocalls"), which may range in purpose from telemarketing to outright fraud, have reached epidemic proportions. While some robocalls are societally useful, there are plenty that are used for malicious purposes. This is particularly concerning because some scam calls steal millions of dollars annually, often from vulnerable populations including the elderly and recent immigrants. Policy mechanisms like the National Do Not Call Registry have failed to meaningfully stop these calls.
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Submitted by Benjamin Fuller on Mon, 03/18/2019 - 3:23pm
Biometrics are part of modern citizens' identity. Individuals' mobile devices collect facial, iris, fingerprint, and electrocardiogram data. Border checkpoints collect travelers' biometrics. National identity cards use biometrics to identify individuals. In many applications, a large group of users' biometrics are stored together in a centralized database. This type of widespread and expanding use of biometrics creates privacy concerns as biometrics are correlated to sensitive attributes such as race, gender, and disease risk factors.
