CyLab researchers earn NSF CAREER awards

Three CyLab Security and Privacy Institute researchers recently earned Faculty Early Career Development Program (CAREER) awards from the National Science Foundation (NSF). The awards are the foundation's most prestigious for young faculty researchers.

Sauvik Das

Sauvik Das, an assistant professor in the Human-Computer Interaction Institute (HCII), earned nearly $594,000 to design and evaluate adversarial machine learning antisurveillance technologies to combat automated identity detection online.

Das will develop a human-centered application that allows users to touch-up images they choose to share online in a manner that helps evade facial recognition and other forms of automated surveillance.

His project will also include educational activities to improve public literacy and knowledge of how to protect images shared online, including webinars and video lectures open to the public and organized in concert with advocacy organizations for populations at higher risk of automated surveillance.
 

Dimitrios Skarlatos

Dimitrios Skarlatos, an assistant professor in the Computer Science Department (CSD), received nearly $588,000 to design and build a virtual memory abstraction that is scalable, heterogenous and secure to meet the current breadth of datacenter computing.

Virtual memory is a cornerstone abstraction of modern computing systems that enables virtualization, programmability and isolation of memory resources. However, existing virtual memory mechanisms were not designed for the current era of datacenter computing and its ample memory capacity, plethora of heterogeneous hardware resources, and abstraction-breaking security vulnerabilities. Skarlatos' research will address these challenges to create more efficient, sustainable and secure datacenters.

The project also includes undergraduate and graduate course offerings, research opportunities, and K-12 outreach activities.
 

Wenting Zheng

Wenting Zheng, an assistant professor in the Computer Science Department (CSD), earned over $596,000 to build a framework for automating multiparty computation (MPC), a cryptographic technique that allows organizations to run complex computations on joint datasets without revealing sensitive inputs to other parties.

Developing efficient MPC protocols is a labor-intensive process that requires cryptographic expertise that is out of reach for most users and developers. Zheng's work will accelerate and democratize the adoption of MPC by designing and building an end-to-end, integrated compiler-runtime framework that automatically generates and executes optimized, workload-specific MPC protocols.

Zheng's work will also develop new graduate courses and outreach programs to make research more accessible to undergraduate students.