James Conners (Brigham Young University), Corey Devenport (Brigham Young University), Stephen Derbidge (Brigham Young University), Natalie Farnsworth (Brigham Young University), Kyler Gates (Brigham Young University), Stephen Lambert (Brigham Young University), Christopher McClain (Brigham Young University), Parker Nichols (Brigham Young University), Daniel Zappala (Brigham Young University)

Passwords have numerous drawbacks, and as a result many systems have been designed to replace them. Password replacements have generally failed to dislodge passwords due to the complexity of balancing usability, deployability, and security. However, despite this lack of success, recent advances with password managers and FIDO2 afford new opportunities to explore system design for password replacements. In this work, we explore the feasibility of a system for user authentication based on certificates. Rather than developing new cryptography, we develop a new *system*, called Let's Authenticate, which combines elements of password managers, FIDO2, and certificates. Our design incorporates feedback from a survey of 397 participants to understand their preferences for system features. Let’s Authenticate issues privacy-preserving certificates to users, automatically manages their credentials, and eliminates trust in third parties. We provide a detailed security and privacy analysis, an overhead analysis, and a systematic comparison of the system to a variety of alternatives using a well-known framework. We discuss how Let’s Authenticate compares to other systems, lessons learned from our design, and issues related to centralized management of authentication data.

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CFInsight: A Comprehensive Metric for CFI Policies

Tommaso Frassetto (Technical University of Darmstadt), Patrick Jauernig (Technical University of Darmstadt), David Koisser (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and...

Wenjia Zhao (Xi'an Jiaotong University and University of Minnesota), Kangjie Lu (University of Minnesota), Qiushi Wu (University of Minnesota), Yong Qi (Xi'an Jiaotong University)

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MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing

Gen Zhang (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Tai Yue (National University of Defense Technology), Xiangdong Kong (National University of Defense Technology), Shan Huang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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