Deepak Sirone Jegan (University of Wisconsin-Madison), Michael Swift (University of Wisconsin-Madison), Earlence Fernandes (University of California San Diego)

A Trigger-action platform (TAP) is a type of distributed system that allows end-users to create programs that stitch their web-based services together to achieve useful automation. For example, a program can be triggered when a new spreadsheet row is added, it can compute on that data and invoke an action, such as sending a message on Slack. Current TAP architectures require users to place complete trust in their secure operation. Experience has shown that unconditional trust in cloud services is unwarranted --- an attacker who compromises the TAP cloud service will gain access to sensitive data and devices for millions of users. In this work, we re-architect TAPs so that users have to place minimal trust in the cloud. Specifically, we design and implement TAPDance, a TAP that guarantees confidentiality and integrity of program execution in the presence of an untrustworthy TAP service. We utilize RISC-V Keystone enclaves to enable these security guarantees while minimizing the trusted software and hardware base. Performance results indicate that TAPDance outperforms a baseline TAP implementation using Node.js with 32% lower latency and 33% higher throughput on average.

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Untangle: Multi-Layer Web Server Fingerprinting

Cem Topcuoglu (Northeastern University), Kaan Onarlioglu (Akamai Technologies), Bahruz Jabiyev (Northeastern University), Engin Kirda (Northeastern University)

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Adryana Hutchinson (The George Washington University), Jinwei Tang (Clark University), Adam Aviv (The George Washington University), Peter Story (Clark University)

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Huiling Chen (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Wenqiang Jin (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Yupeng Hu (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Zhenyu Ning (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Kenli Li (College…

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Artur Hermann, Natasa Trkulja (Ulm University - Institute of Distributed Systems), Anderson Ramon Ferraz de Lucena, Alexander Kiening (DENSO AUTOMOTIVE Deutschland GmbH), Ana Petrovska (Huawei Technologies), Frank Kargl (Ulm University - Institute of Distributed Systems)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)