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Secure Authentication in Blockchain Environments

Guilio 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...

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...

How Do Vulnerable Patients Understand Data Privacy as It Pertains to mHealth Interventions?

Laura Gomez-Pathak, PhD Student, School of Social Welfare, UC Berkeley
As mobile health (mHealth) interventions have the potential to acquire a dominant role in safety-net healthcare settings, there are many challenges to data privacy that need to be considered. Users with marginalized backgrounds have a greater risk of experiencing more detrimental consequences of privacy and security breaches to mHealth apps...

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...

Novel Metrics for Robust Machine Learning

Michael Mahoney, Professor, Department of Statistics, UC Berkeley
Benjamin Erichson, Postdoctoral Scholar, Department of Statistics, UC Berkeley
Although deep neural networks (DNNs) have achieved impressive performance in several applications, they also exhibit several well-known sensitivities and security concerns that can emerge for a variety of reasons, including adversarial attacks, backdoor attacks, and lack of fairness in classification. Hence, it is important to better understand these risks in...

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...

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...

Privacy Controls for Always-Listening Devices

Nathan Malkin, PhD Student, Department of Electrical Engineering and Computer Science, UC Berkeley
Intelligent voice assistants and other microphone-equipped Internet of Things devices offer great convenience at the cost of very high privacy risks. The goal of our research is to develop privacy controls for devices that listen all the time — beyond a few specific keywords. More specifically, our goal is for...

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...

Factors Affecting Trust Among Vulnerable Populations

Rajasi Desai, Graduate Student Researcher, School of Information, UC Berkeley
Varshine Chandrakanthan, Graduate Student Researcher, School of Information, UC Berkeley
This project aims to understand the trust dynamics and the factors affecting trust for vulnerable populations like human rights defenders, activists, and journalists who document and upload sensitive media, as well as people who receive this media in order to use it as evidence. The researchers will work to understand...
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