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Evaluating equity and bias in cybersecurity related job descriptions and the impact on the cyber talent pipeline

Mehtabk Khan, JSD Student, School of Law, UC Berkeley
Cybersecurity workers are in high demand but short supply. During the Covid-19 crisis, we have seen a greater need for cybersecurity professionals as e-commerce has skyrocketed, universities have shifted online, and millions of Americans are working from home on personal networks. There are also significant diversity challenges to the cybersecurity...

Evaluating The Digital Divide in The Usability of Privacy and Security Settings in Smartphones

Joanne Ma, Graduate Student, School of Information, UC Berkeley
Alisa Frik, Postdoctoral Researcher, International Computer Science Institute, UC Berkeley
With the smartphone penetration rate reaching over 80% in the US, smartphone settings remain one of the main models for information privacy and security controls. Yet, their usability is largely understudied, especially with respect to the usability impact on underrepresented socio-economic and low-tech groups. In this project we will estimate...

Robust Machine Learning via Random Transformation

Chawin Sitawarin, PhD Student, EECS, UC Berkeley
Current machine learning models suffer from evasion attacks such as adversarial examples. This introduces security and safety concerns that lack any clear solution. Recently, the usage of random transformations has emerged as a promising defense against the attack. Here, we hope to extend this general idea to build a defense...

Misinformation Corrections

Ji Su Yoo, PhD Student, School of Information, UC Berkeley
Misinformation and disinformation campaigns often rely on bots and fake accounts to impersonate human users with similar demographic characteristics, political beliefs, and social values as their audience to establish credibility. Such nefarious efforts are successful because human beliefs and behaviors about new information are based on the identity of the...

Secure Authentication in Blockchain Environments

Giulio Malavolta, Postdoctoral Fellow, Computer Science Department, Carnegie Mellon University
Bitcoin and blockchain systems brought us to the brink of a technological revolution: these systems allow us to bypass the need for centralized trusted entities to run protocols on a large scale. However, the decentralized nature of these systems brings unique challenges, including user authentication. While cryptography provides strong solutions...

Law Enforcement Access to Digital Data: Understanding the Everyday Processes

Yan Fang, PhD Student, School of Law, Jurisprudence and Social Policy, UC Berkeley
During criminal investigations, U.S. law enforcement agencies often seek evidence held by third-party businesses. Many of these companies have established policies on how to respond to law enforcement requests for information. How do government agencies navigate these policies? This project studies this question through semi-structured interviews with investigators and prosecutors...

Secure Machine Learning

David Wagner, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley
We will study how to harden machine learning classifiers against adversarial attack. We will explore general mechanisms for making deep-learning classifiers more robust against attack, with a special focus on security for autonomous vehicles. Current schemes fail badly in the presence of an attacker who is trying to fool or...

Keystone: An Open Framework for Architecting TEEs

Dawn Song, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley
Shweta Shivaji Shinde, Postdoctoral Scholar, Department of Electrical Engineering and Computer Science, UC Berkeley
David Kohlbrenner, Postdoctoral Scholar, Department of Electrical Engineering and Computer Science, UC Berkeley
Trusted execution environments (TEEs) are found in a range of devices — from embedded sensors to cloud servers — and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs, with little...

Measuring and Defending Against New Trends in Nation-State Surveillance of Dissidents

William Marczak, Senior Research Fellow|Co-founder|Postdoctoral Fellow, Citizen Lab|Bahrain Watch|UC Berkeley
Targeted nation-state hacking against dissidents’ devices and online accounts is a growing problem with significant real-world consequences for targets, including physical harm. While initial research efforts have mapped out part of the ecosystem of these attacks, attackers are increasingly “going dark” by adapting their tools and techniques to compromise target...

Obscuring Authorship: Neural Methods for Adversarial Stylometry and Text-Based Differential Privacy

Matthew Sims, Postdoctoral Scholar and Lecturer, School of Information, UC Berkeley
The continual improvement of models for author attribution—the task of inferring the author of an anonymized document—indicates potential benefits but also substantial risks in the context of privacy and cybersecurity. Such improvements pose particular threats to whistleblowers and other individuals who might have strong political or security-related reasons for wanting...
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