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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An In-depth Analysis of Duplicated Linux Kernel Bug Reports

Dongliang Mu (Huazhong University of Science and Technology), Yuhang Wu (Pennsylvania State University), Yueqi Chen (Pennsylvania State University), Zhenpeng Lin (Pennsylvania State University), Chensheng Yu (George Washington University), Xinyu Xing (Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign)

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Tetrad: Actively Secure 4PC for Secure Training and Inference

Nishat Koti (IISc Bangalore), Arpita Patra (IISc Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (IISc, Bangalore)

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CFInsight: A Comprehensive Metric for CFI Policies

Tommaso Frassetto (Technical University of Darmstadt), Patrick Jauernig (Technical University of Darmstadt), David Koisser (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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Problematic Content in Online Ads

Franzisca Roesner (University of Washington)

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