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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Proof of Backhaul: Trustfree Measurement of Broadband Bandwidth

Peiyao Sheng (Kaleidoscope Blockchain Inc.), Nikita Yadav (Indian Institute of Science), Vishal Sevani (Kaleidoscope Blockchain Inc.), Arun Babu (Kaleidoscope Blockchain Inc.), Anand Svr (Kaleidoscope Blockchain Inc.), Himanshu Tyagi (Indian Institute of Science), Pramod Viswanath (Kaleidoscope Blockchain Inc.)

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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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FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Meenatchi Sundaram Muthu Selva Annamalai (University College London), Igor Bilogrevic (Google), Emiliano De Cristofaro (University of California, Riverside)

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IRRedicator: Pruning IRR with RPKI-Valid BGP Insights

Minhyeok Kang (Seoul National University), Weitong Li (Virginia Tech), Roland van Rijswijk-Deij (University of Twente), Ted "Taekyoung" Kwon (Seoul National University), Taejoong Chung (Virginia Tech)

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