  We consider three important challenges in conference peer review: (i)
reviewers maliciously attempting to get assigned to certain papers to provide
positive reviews, possibly as part of quid-pro-quo arrangements with the
authors; (ii) "torpedo reviewing," where reviewers deliberately attempt to get
assigned to certain papers that they dislike in order to reject them; (iii)
reviewer de-anonymization on release of the similarities and the
reviewer-assignment code. On the conceptual front, we identify connections
between these three problems and present a framework that brings all these
challenges under a common umbrella. We then present a (randomized) algorithm
for reviewer assignment that can optimally solve the reviewer-assignment
problem under any given constraints on the probability of assignment for any
reviewer-paper pair. We further consider the problem of restricting the joint
probability that certain suspect pairs of reviewers are assigned to certain
papers, and show that this problem is NP-hard for arbitrary constraints on
these joint probabilities but efficiently solvable for a practical special
case. Finally, we experimentally evaluate our algorithms on datasets from past
conferences, where we observe that they can limit the chance that any malicious
reviewer gets assigned to their desired paper to 50% while producing
assignments with over 90% of the total optimal similarity. Our algorithms still
achieve this similarity while also preventing reviewers with close associations
from being assigned to the same paper.
