Anonymous peer review protects reviewers from retaliation, yet reviewer anonymity may also be weakened by artifacts released through the review process itself. This risk is growing as many venues require submitting authors to review, creating substantial overlap between authors and reviewers within the same review cycle. We identify review-to-submission linkage, where an anonymous review can be associated with the reviewer's own submission, substantially shrinking the reviewer anonymity set. To assess whether this linkage is practically exploitable, we develop ECHO, which combines technical-expertise and writing-style signals across reviews, papers, and rebuttals, and evaluate it on R2S-Bench, a ground-truth benchmark we construct. ECHO correctly identifies the reviewer's own submission in Top-1 for 34.6% of reviews, in the Top-5 for 54.0%, and in the Top-10 for 63.4%, compared with 0.5%, 2.6%, and 5.3% under random ranking, from the candidate submissions. With a stronger Stage-2 reranker, ECHO further reaches 56.4% Top-1 and 66.4% Top-10. These results demonstrate that review-to-submission linkage is feasible using only routinely released peer-review artifacts. Code and benchmark are available at https://github.com/kaimu11111/ECHO-Revealing-Review-to-Submission-Linkage-in-Anonymous-Peer-Review.

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