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Reverse Engineer and Counter Adversarial Attacks with Unsupervised Representation Learning

Xudong Wang, PhD Student, EECS, UC Berkeley
Nils Worzyk, Postdoctoral Researcher, EECS, UC Berkeley
Computer vision has been integrated into many areas of our lives, including facial recognition, augmented reality, autonomous driving, and healthcare. However, making them more accurate and generalizing to real world data alone is no longer sufficient, we have to safe-guard their robustness against malicious attacks in cyberspace. Compared with the...

Are Password Managers Improving our Password Habits?

David Ng, Graduate Student, School of Information, UC Berkeley
Cristian Bravo-Lillo, Lecturer, School of Information, UC Berkeley
Jacky Ho, Graduate Student, School of Information, UC Berkeley
Christian Hercules, Graduate Student, School of Information, UC Berkeley
Stuart Schechter, Lecturer, School of Information, UC Berkeley
Password managers adoptions are becoming the norm these days, but are they also encouraging best practices for its users? Do they use complex and unique passwords or do they just store weak passwords? We discovered that many users ignore password reset notifications. We are motivated to find a path to...

Towards Bayesian Classifiers that are Robust Against Adversarial Attacks

An Ju, PhD Student, EECS, UC Berkeley
We aim to build neural networks that are intrinsically robust against adversarial attacks. We focus on classifying images in real-world scenarios with complex backgrounds under unforeseen adversarial attacks. Previous defenses lack interpretability and have limited robustness against unforeseen attacks, failing to deliver trustworthiness to users. We will study Bayesian models,...

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...

A Comprehensive Investigation of Developers’ Remediation Practices

Noura Alomar, PhD Student, International Computer Science Institute, UC Berkeley
Primal Wijesekera, Staff Research Scientist, International Computer Science Institute, UC Berkeley
Security vulnerabilities pose a grave danger to the integrity of any system because they can undermine almost any protection mechanism organizations put in place to defend themselves against potential attacks. As such, finding vulnerabilities before the software gets deployed or after putting software in production is a critical task in...

Assessing and Developing Online Election Information Infrastructure

Emma Lurie, PhD Student, School of Information, UC Berkeley
In the United States, people are increasingly turning to online sources to find information about elections. Election information includes everything from mail-in ballot instructions to candidate Facebook page posts. In the U.S., as well as around the world, online misinformation threatens democratic systems. Politicians, technology companies, journalists, and voters all...

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...

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

Mehtab 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...

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...

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...
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