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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...

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...

Re-imagining Password Management for Low-Technology Proficiency Users

Ching-Yi Lin, Graduate Student Researcher, School of Information, UC Berkeley
Ayo Animashaun, Graduate Student Researcher, School of Information, UC Berkeley
Jing Wu, Graduate Student Researcher, School of Information, UC Berkeley
Amy Huang, Graduate Student Researcher, School of Information, UC Berkeley
Passwords and login information control access to some of the most important aspects of life, such as banking and finances, medical services, and other sensitive personal information. According to Pew Research, 44% of online adults ages 30 to 64 say they have a hard time keeping track of their passwords....

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...

Privacy for Always-Listening Devices

David Wagner, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley
Serge Egelman, Research Director, International Computer Science Institute, UC Berkeley
Microphone-equipped Internet of Things devices, and smart voice assistants specifically, offer the promise of great convenience, yet pose grave privacy challenges. The aim of our research is to understand the privacy implications of voice as a sensitive data source and develop techniques to help users protect their privacy from these...

Engaging Expert Stakeholders about the Future of Menstrual Biosensing Technology

Noura Howell, PhD Candidate, School of Information, UC Berkeley
Richmond Wong, PhD Candidate, School of Information, UC Berkeley
Sarah Fox, Postdoctoral Scholar, Department of Communication and The Design Lab, UC San Diego
Franchesca Spektor, Undergraduate Student Researcher, UC Berkeley
Networked sensor technologies are increasingly present in daily life. While promising improved health and efficiency, they also introduce far-reaching issues around cybersecurity, privacy, autonomy, and consent that can be difficult to predict or resist. This project will examine menstrual tracking technologies as a case for understanding the current and near-future...

Using Multidisciplinary Design to Improve AI/ML Cybersecurity Scenarios

James Pierce, Researcher/Assistant Professor of Design, CITRIS and the Banatao Institute, UC Berkeley|California College of the Arts
Richmond Wong, PhD Candidate, School of Information, UC Berkeley
Tara Shi, Graduate Student Researcher, College for Environmental Design, UC Berkeley
The overarching research question guiding this project is: How can multidisciplinary design methods, perspectives, and forms be applied to improve existing artificial intelligence (AI) cybersecurity scenarios, predictions, and extrapolations produced by researchers, market analysts, government organizations, and industry experts? This research will begin by collecting and carefully reviewing and organizing...

Privacy-preserving and Decentralized Federated Learning

Dawn Song, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley
Min Du, Postdoctoral Fellow, Department of Electrical Engineering and Computer Science, UC Berkeley
Jian Liu, Postdoctoral Fellow, Department of Electrical Engineering and Computer Science, UC Berkeley
Machine learning technology is developing rapidly and has been continuously changing our daily life. However, a major limiting factor that hinders many machine learning tasks is the need of huge and diverse training data. Crowdsourcing has been shown effective to collect data labels with a centralized server. The emergence of...

Towards Efficient Data Economics: Decentralized Data Marketplace and Smart Pricing Models

Dawn Song, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley
Ruoxi Jia, Postdoctoral Researcher, Department of Electrical Engineering and Computer Science, UC Berkeley
Advances in machine learning and artificial intelligence have demonstrated enormous potential for building intelligent systems and growing knowledge bases. However, the current data marketplaces are not efficient enough to facilitate long-term technological and economic advancements. An efficient data market would allow participants to strategically sell or purchase data and get...

Hackers vs. Testers: Understanding Software Vulnerability Discovery Processes

Primal Wijesekera, Staff Research Scientist, International Computer Science Institute, UC Berkeley
Serge Egelman, Research Director, International Computer Science Institute, UC Berkeley
Noura Alomar, PhD Student, International Computer Science Institute, UC Berkeley
Amit Elazari, Lecturer/Director, School of Information, UC Berkeley|Intel
Security vulnerabilities pose a grave danger to the integrity of any system because they can undermine almost any protection mechanism or safeguard. As such, finding vulnerabilities before the software gets deployed is a critical task in any current software development cycle. A vital tool has recently emerged in the arsenal...
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