论文标题
部分可观测时空混沌系统的无模型预测
Calibration with Privacy in Peer Review
论文作者
论文摘要
同行评审中的审阅者经常被错误地校准：它们可能是严格的，宽松的，极端的，中等的。先前已经提出了许多算法来校准评论。但是，这种校准的尝试可以泄漏有关哪个审阅者审查哪些论文的敏感信息。在本文中，我们确定了使用隐私的校准问题，并提供了解决该问题的基础构建基础。具体而言，我们在一个简化的挑战模型下介绍了该问题的理论研究，涉及两位审阅者，两篇论文和一个贴图的对手。我们的主要结果建立了隐私（防止对手推断审稿人身份）和效用（接受更好的论文）和设计明确的计算有效算法之间的权衡之间的帕累托前沿，我们证明这是帕累托最佳的。
Reviewers in peer review are often miscalibrated: they may be strict, lenient, extreme, moderate, etc. A number of algorithms have previously been proposed to calibrate reviews. Such attempts of calibration can however leak sensitive information about which reviewer reviewed which paper. In this paper, we identify this problem of calibration with privacy, and provide a foundational building block to address it. Specifically, we present a theoretical study of this problem under a simplified-yet-challenging model involving two reviewers, two papers, and an MAP-computing adversary. Our main results establish the Pareto frontier of the tradeoff between privacy (preventing the adversary from inferring reviewer identity) and utility (accepting better papers), and design explicit computationally-efficient algorithms that we prove are Pareto optimal.
