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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WIP: A Trust Assessment Method for In-Vehicular Networks using...

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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VETEOS: Statically Vetting EOSIO Contracts for the “Groundhog Day”...

Levi Taiji Li (University of Utah), Ningyu He (Peking University), Haoyu Wang (Huazhong University of Science and Technology), Mu Zhang (University of Utah)

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Understanding Route Origin Validation (ROV) Deployment in the Real...

Lancheng Qin (Tsinghua University, BNRist), Li Chen (Zhongguancun Laboratory), Dan Li (Tsinghua University, Zhongguancun Laboratory), Honglin Ye (Tsinghua University), Yutian Wang (Tsinghua University)

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Predictive Context-sensitive Fuzzing

Pietro Borrello (Sapienza University of Rome), Andrea Fioraldi (EURECOM), Daniele Cono D'Elia (Sapienza University of Rome), Davide Balzarotti (Eurecom), Leonardo Querzoni (Sapienza University of Rome), Cristiano Giuffrida (Vrije Universiteit Amsterdam)

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