Data science

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Visible to the public CAREER: Scaling Forensic Algorithms for Big Data and Adversarial Environments

Forged digital images or video can threaten reputations or impede criminal justice, due to falsified evidence. Over the past decade, researchers have developed a new class of security techniques known as 'multimedia forensics' to determine the origin and authenticity of multimedia information, such as potentially falsified images or videos. However, the proliferation of smartphones and the rise of social media have led to an overwhelming increase in the volume of multimedia information that must be forensically authenticated.

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Visible to the public TWC: Phase: Medium: Collaborative Proposal: Understanding and Exploiting Parallelism in Deep Packet Inspection on Concurrent Architectures

Deep packet inspection (DPI) is a crucial tool for protecting networks from emerging and sophisticated attacks. However, it is becoming increasingly difficult to implement DPI effectively due to the rising need for more complex analysis, combined with the relentless growth in the volume of network traffic that these systems must inspect. To address this challenge, future DPI technologies must exploit the power of emerging highly concurrent multi- and many-core platforms.

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Visible to the public TWC: Small: Physiological Information Leakage: A New Front on Health Information Security

With the growing use of implantable and wearable medical devices, information security for such devices has become a major concern. Prior work in this area mostly focuses on attacks on the wireless communication channel among these devices and health data stored in online databases. The proposed work is a departure from this line of research and is motivated by acoustic and electromagnetic physiological information leakage from the medical devices. This type of information leakage can also directly occur from the human body, thus raising privacy concerns.