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A major impediment to research on improving peer review is the unavailabilityof peer-review data, since any release of such data must grapple with thesensitivity of the peer review data in terms of protecting identities ofreviewers from authors. We posit the need to develop techniques to releasepeer-review data in a privacy-preserving manner. Identifying this problem, inthis paper we propose a framework for privacy-preserving release of certainconference peer-review data -- distributions of ratings, miscalibration, andsubjectivity -- with an emphasis on the accuracy (or utility) of the releaseddata. The crux of the framework lies in recognizing that a part of the datapertaining to the reviews is already available in public, and we use thisinformation to post-process the data released by any privacy mechanism in amanner that improves the accuracy (utility) of the data while retaining theprivacy guarantees. Our framework works with any privacy-preserving mechanismthat operates via releasing perturbed data. We present several positive andnegative theoretical results, including a polynomial-time algorithm forimproving on the privacy-utility tradeoff.

On the Privacy-Utility Tradeoff in Peer-Review Data Analysis