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: Hidden Hub Eavesdropping Attack in Matter-enabled Smart Home...

Song Liao, Jingwen Yan, Long Cheng (Clemson University)

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ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

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Bernoulli Honeywords

Ke Coby Wang (Duke University), Michael K. Reiter (Duke University)

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