Ioanna Tzialla (New York University), Abhiram Kothapalli (Carnegie Mellon University), Bryan Parno (Carnegie Mellon University), Srinath Setty (Microsoft Research)

This paper introduces Verdict, a transparency dictionary, where an untrusted service maintains a label-value map that clients can query and update (foundational infrastructure for end-to-end encryption and other applications). To prevent unauthorized modifications to the dictionary, for example, by a malicious or a compromised service provider, Verdict produces publicly-verifiable cryptographic proofs that it correctly executes both reads and authorized updates. A key advance over prior work is that Verdict produces efficiently-verifiable proofs while incurring modest proving overheads. Verdict accomplishes this by composing indexed Merkle trees (a new SNARK-friendly data structure) with Phalanx (a new SNARK that supports amortized constant-sized proofs and leverages particular workload characteristics to speed up the prover). Our experimental evaluation demonstrates that Verdict scales to dictionaries with millions of labels while imposing modest overheads on the service and clients.

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Demo #12: Too Afraid to Drive: Systematic Discovery of...

Ziwen Wan (UC Irvine), Junjie Shen (UC Irvine), Jalen Chuang (UC Irvine), Xin Xia (UCLA), Joshua Garcia (UC Irvine), Jiaqi Ma (UCLA) and Qi Alfred Chen (UC Irvine)

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ditto: WAN Traffic Obfuscation at Line Rate

Roland Meier (ETH Zürich), Vincent Lenders (armasuisse), Laurent Vanbever (ETH Zürich)

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Hybrid Trust Multi-party Computation with Trusted Execution Environment

Pengfei Wu (School of Computing, National University of Singapore), Jianting Ning (College of Computer and Cyber Security, Fujian Normal University; Institute of Information Engineering, Chinese Academy of Sciences), Jiamin Shen (School of Computing, National University of Singapore), Hongbing Wang (School of Computing, National University of Singapore), Ee-Chien Chang (School of Computing, National University of Singapore)

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Demo #13: Attacking LiDAR Semantic Segmentation in Autonomous Driving

Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

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