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November 1, 2020 Mathieu Baudet, George Danezis, Alberto Sonnino
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FastPay: High-Performance Byzantine Fault Tolerant Settlement

FastPay allows a set of distributed authorities, some of which are Byzantine, to maintain a high-integrity and availability settlement system for pre-funded payments. It can be used to settle payments in a native unit of value (crypto-currency), or as a financial side-infrastructure to support retail payments in fiat currencies. FastPay is based on Byzantine Consistent Broadcast as its core primitive, foregoing the expenses of full atomic commit channels (consensus).
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October 22, 2020 Kostas Chalkias, François Garillot, Valeria Nikolaenko
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Taming the many EdDSAs

This paper analyses security of concrete instantiations of EdDSA by identifying exploitable inconsistencies between standardization recommendations and Ed25519 implementations. We mainly focus on current ambiguity regarding signature verification equations, binding and malleability guarantees, and incompatibilities between randomized batch and single verification.
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October 15, 2020 Fedor Kozlov, Isabella Yuen, Jakub Kowalczyk, Daniel Bernhardt, David Freeman, Paul Pearce, Ivan Ivanov
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Evaluating Changes to Fake Account Verification Systems

This paper proposes a method of assessing the effectiveness of experimental iterations for OSN verification systems, and presents an evaluation of this method against human- labelled ground truth data using production Facebook data.
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August 24, 2020 Viet-An Nguyen, Peibei Shi, Jagdish Ramakrishnan, Udi Weinsberg, Henry C. Lin, Steve Metz, Neil Chandra, Jane Jing, Dimitris Kalimeris
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CLARA: Confidence of Labels and Raters

In this paper, we present CLARA (Confidence of Labels and Raters), a system developed and deployed at Facebook for aggregating reviewer decisions and estimating their uncertainty. We perform extensive validations and describe the deployment of CLARA for measuring the base rate of policy violations, quantifying reviewers’ performance, and improving their efficiency.
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August 12, 2020 Frank Li
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Shim Shimmeny: Evaluating the Security and Privacy Contributions of Link Shimming in the Modern Web

In this paper, we provide a large-scale empirical evaluation of link shimming’s security and privacy contributions, using Facebook’s real-world deployment as a case study. Our results indicate that even in the modern web, link shimming can provide meaningful security and privacy benefits to users broadly.
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August 11, 2020 Katriel Cohn-Gordon, Georgios Damaskinos, Divino Neto, Joshi Cordova, Benoît Reitz, Benjamin Strahs, Daniel Obenshain, Paul Pearce, Ioannis Papagiannis
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DELF: Safeguarding deletion correctness in Online Social Networks

We propose DELF, a deletion framework for modern OSNs. In DELF, developers specify deletion annotations on data type definitions, which the framework maps into asynchronous, reliable and temporarily reversible operations on backing data stores. DELF validates annotations both statically and dynamically, proactively flagging errors and suggesting fixes.
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May 31, 2020 Ben Goldberger, Yossi Adi, Joseph Keshet, Guy Katz
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Minimal Modifications of Deep Neural Networks using Verification

We leverage recent advances in DNN verification and propose a technique for modifying a DNN according to certain requirements, in a way that is provably minimal, does not require any retraining, and is thus less likely to affect other aspects of the DNN’s behavior.
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May 14, 2020 John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Shan He, Ralf Lämmel, Erik Meijer, Silvia Sapora, Justin Spahr-Summers
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Ownership at Large – Open Problems and Challenges in Ownership Management

This paper sets out open problems and challenges on ownership for the research community with advances expected from the fields of software engineering, programming languages, and machine learning.
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May 14, 2020 Ralf Lämmel, Alvin Kerber, Liane Praza
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Understanding What Software Engineers Are Working On

In this paper, we explain the corresponding ‘work-item prediction challenge’ on the grounds of representative scenarios, report on related efforts at Facebook, discuss some lessons learned, and review related work to call to arms to leverage, advance, and combine techniques from program comprehension, mining software repositories, process mining, and machine learning.
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April 29, 2020 John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Ralf Lämmel, Erik Meijer, Silvia Sapora, Justin Spahr-Summers
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WES: Agent-based User Interaction Simulation on Real Infrastructure

We introduce the Web-Enabled Simulation (WES) research agenda, and describe FACEBOOK’s WW system. We describe the application of WW to reliability, integrity and privacy at FACEBOOK1, where it is used to simulate social media interactions on an infrastructure consisting of hundreds of millions of lines of code.
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