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.

翻译：同侪审查的审查者往往被错误地校准:他们可能是严格、宽大、极端、温和的。以前曾提议过一些算法来校准审查。 但是,这种校准尝试会泄露关于审查者审查哪份文件的敏感信息。 在本文中,我们找出了隐私校准问题,并提供了解决这一问题的基础基础。 具体地说,我们根据一个简化但有争议的模式,提出了这一问题的理论研究,其中涉及两名审查者、两份论文和一个MAP-compecting对手。我们的主要结果确定了隐私(防止对手推断审查者身份)和效用(接受更好的文件)之间的平衡界限,并设计了我们证明是最佳的明确的计算效率算法。