论文标题
关于同行评审数据分析中的隐私 - 私人权衡权衡
On the Privacy-Utility Tradeoff in Peer-Review Data Analysis
论文作者
论文摘要
对改进同行评审的研究的主要障碍是同行评审数据的不可用，因为此类数据的任何释放都必须在保护审稿人从作者身上保护审阅者身份方面应对同行评审数据的敏感性。我们提出需要以隐私保护方式来开发技术以发布同行评审数据的技术。确定这个问题，在本文中，我们提出了一个框架，以发布某些会议同行评审数据的隐私发布 - 评级，错误校准和主观性的分布，重点是发布数据的准确性（或实用程序）。该框架的症结在于认识到与评论有关的一部分数据已经在公开场合可用，我们使用此信息来后处理任何隐私机制发布的数据，以提高数据的准确性（实用性），同时保留隐私保证。我们的框架可与任何通过释放扰动数据运行的隐私机制合作。我们提出了几个积极和负面的理论结果，包括用于改善隐私 - 私人权衡的多项式时间算法。
A major impediment to research on improving peer review is the unavailability of peer-review data, since any release of such data must grapple with the sensitivity of the peer review data in terms of protecting identities of reviewers from authors. We posit the need to develop techniques to release peer-review data in a privacy-preserving manner. Identifying this problem, in this paper we propose a framework for privacy-preserving release of certain conference peer-review data -- distributions of ratings, miscalibration, and subjectivity -- with an emphasis on the accuracy (or utility) of the released data. The crux of the framework lies in recognizing that a part of the data pertaining to the reviews is already available in public, and we use this information to post-process the data released by any privacy mechanism in a manner that improves the accuracy (utility) of the data while retaining the privacy guarantees. Our framework works with any privacy-preserving mechanism that operates via releasing perturbed data. We present several positive and negative theoretical results, including a polynomial-time algorithm for improving on the privacy-utility tradeoff.
