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October 19, 2021 Antti E. J. Hyvärinen, Matteo Marescotti, Natasha Sharygina
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Lookahead in Partitioning SMT

This paper addresses both of these observations. We give a hybrid algorithm that integrates lookahead into the state-based representation of an SMT solver and show that in the vast majority of cases it is possible to compute full lookahead up to depth four on inexpensive theories.
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August 1, 2021 Nasser Aldaghri, Hessam Mahdavifar, Ahmad Beirami
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Coded Machine Unlearning

Our experimental results show that the proposed coded machine unlearning provides a better performance versus unlearning cost trade-off compared to the uncoded baseline.
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July 31, 2021 Eleftherios Kokoris-Kogias, Enis Ceyhun Alp, Linus Gasser, Philipp Jovanovic, Ewa Syta, Bryan Ford
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CALYPSO: Private Data Management for Decentralized Ledgers

This work enhances permissioned and permissionless blockchains with the ability to manage confidential data without forfeiting availability or decentralization.
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July 19, 2021 Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
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Making Paper Reviewing Robust to Bid Manipulation Attacks

We develop a novel approach for paper bidding and assignment that is much more robust against such attacks. We show empirically that our approach provides robustness even when dishonest reviewers collude, have full knowledge of the assignment system’s internal workings, and have access to the system’s inputs.
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July 16, 2021 Konstantinos (Kostas) Chalkias, Shir Cohen, Kevin Lewi, Fredric Moezinia, Yolan Romailler
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HashWires: Hyperefficient Credential-Based Range Proofs

This paper presents HashWires, a hash-based range proof protocol that is applicable in settings for which there is a trusted third party (typically a credential issuer) that can generate commitments. We refer to these as “credential-based” range proofs (CBRPs). HashWires improves upon hashchain solutions that are typically restricted to micro-payments for small interval ranges, achieving an exponential speedup in proof generation and verification time.
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June 16, 2021 Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen
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Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption

This paper presents Porcupine, an optimizing compiler that generates vectorized HE code using program synthesis. HE poses three major compilation challenges: it only supports a limited set of SIMD-like operators, it uses long-vector operands, and decryption can fail if ciphertext noise growth is not managed properly. Porcupine captures the underlying HE operator behavior so that it can automatically reason about the complex trade-offs imposed by these challenges to generate optimized, verified HE kernels.
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May 12, 2021 Sam Blackshear, Konstantinos (Kostas) Chalkias, Panagiotis Chatzigiannis, Riyaz Faizullabhoy, Irakliy Khaburzaniya, Lefteris Kokoris Kogias, Joshua Lind, David Wong, Tim Zakian
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Reactive Key-Loss Protection in Blockchains

We present a novel approach for blockchain asset owners to reclaim their funds in case of accidental private-key loss or transfer to a mistyped address. Our solution can be deployed upon failure or absence of proactively implemented backup mechanisms, such as secret sharing and cold storage.
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May 1, 2021 Brandon Reagen, Wooseok Choi, Yeongil Ko, Vincent T. Lee, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks
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Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference

This paper introduces Cheetah, a set of algorithmic and hardware optimizations for server-side HE DNN inference to approach real-time speeds. Cheetah proposes HE-parameter tuning optimization and operator scheduling optimizations, which together deliver 79× speedup over state-of-the-art.
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February 22, 2021 Jeremiah Onaolapo, Nektarios Leontiadis, Despoina Magka, Gianluca Stringhini
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SocialHEISTing: Understanding Stolen Facebook Accounts

To understand the effects of demographic attributes on attacker behavior in stolen social accounts, we devised a method to instrument and monitor such accounts. We then created, instrumented, and deployed more than 1000 Facebook accounts, and exposed them to criminals.
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January 18, 2021 Chuan Guo, Awni Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu
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Secure multiparty computations in floating-point arithmetic

Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares).
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