Ka Fun Tang (The Chinese University of Hong Kong), Che Wei Tu (The Chinese University of Hong Kong), Sui Ling Angela Mak (The Chinese University of Hong Kong), Sze Yiu Chau (The Chinese University of Hong Kong)

Various email protocols, including IMAP, POP3, and SMTP, were originally designed as “plaintext” protocols without inbuilt confidentiality and integrity guarantees. To protect the communication traffic, TLS can either be used implicitly before the start of those email protocols, or introduced as an opportunistic upgrade in a post-hoc fashion. In order to improve user experience, many email clients nowadays provide a so-called “auto-detect” feature to automatically determine a functional set of configuration parameters for the users. In this paper, we present a multifaceted study on the security of the use of TLS and auto-detect in email clients. First, to evaluate the design and implementation of client-side TLS and auto-detect, we tested 49 email clients and uncovered various flaws that can lead to covert security downgrade and exposure of user credentials to attackers. Second, to understand whether current deployment practices adequately avoid the security traps introduced by opportunistic TLS and auto-detect, we collected and analyzed 1102 email setup guides from academic institutes across the world, and observed problems that can drive users to adopt insecure email settings. Finally, with the server addresses obtained from the setup guides, we evaluate the sever-side support for implicit and opportunistic TLS, as well as the characteristics of their certificates. Our results suggest that many users suffer from an inadvertent loss of security due to careless handling of TLS and auto-detect, and organizations in general are better off prescribing concrete and detailed manual configuration to their users.

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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VoiceRadar: Voice Deepfake Detection using Micro-Frequency and Compositional Analysis

Kavita Kumari (Technical University of Darmstadt), Maryam Abbasihafshejani (University of Texas at San Antonio), Alessandro Pegoraro (Technical University of Darmstadt), Phillip Rieger (Technical University of Darmstadt), Kamyar Arshi (Technical University of Darmstadt), Murtuza Jadliwala (University of Texas at San Antonio), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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Detecting Ransomware Despite I/O Overhead: A Practical Multi-Staged Approach

Christian van Sloun (RWTH Aachen University), Vincent Woeste (RWTH Aachen University), Konrad Wolsing (RWTH Aachen University & Fraunhofer FKIE), Jan Pennekamp (RWTH Aachen University), Klaus Wehrle (RWTH Aachen University)

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Secure Transformer Inference Made Non-interactive

Jiawen Zhang (Zhejiang University), Xinpeng Yang (Zhejiang University), Lipeng He (University of Waterloo), Kejia Chen (Zhejiang University), Wen-jie Lu (Zhejiang University), Yinghao Wang (Zhejiang University), Xiaoyang Hou (Zhejiang University), Jian Liu (Zhejiang University), Kui Ren (Zhejiang University), Xiaohu Yang (Zhejiang University)

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