Demo Data from EDBT 2026 / reviewers in the wild / expert
Forest Zhang
dblp:329/5047
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Cryptographic protocols and secure computation · 100% | |
| Theoretical computer science
1 paper |
Algorithms and data structures · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic protocols and secure computation
coin flipping |
0.9 | 1 | 2025 | Game-Theoretically Fair Coin Toss with Arbitrary Preferences · ASIACRYPT (8) 2025 |
Cryptographic protocols and secure computation › fair exchange
game-theoretic fairness |
0.9 | 1 | 2025 | Game-Theoretically Fair Coin Toss with Arbitrary Preferences · ASIACRYPT (8) 2025 |
Algorithms and data structures
coin tossing |
0.9 | 1 | 2025 | Game-Theoretically Fair Coin Toss with Arbitrary Preferences · ASIACRYPT (8) 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Game-Theoretically Fair Coin Toss with Arbitrary Preferences
Forest Zhang, Ke Wu 0001 |
ASIACRYPT (8) | 1 |
| 2023 | Opponent Indifference in Rating Systems: A Theoretical Case for SonasabstractIn competitive games, it is common to assign each player a real number rating signifying their skill level. A rating system is a procedure by which player ratings are adjusted upwards each time they win, or downwards each time they lose. Many matchmaking systems give players some control over their opponent’s rating; for example, a player might be able to selectively initiate games against opponents whose ratings are publicly visible, or abort a game without penalty before it begins but after glimpsing their opponent’s rating. It is natural to ask whether one can design a rating system that does not incentivize a rating-maximizing player to act strategically, seeking games against opponents of one rating over another. We show the following: - The full version of this "opponent indifference" property is unfortunately too strong to be feasible. Although it is satisfied by some rating systems, these systems lack certain desirable expressiveness properties, suggesting that they are not suitable to capture most games of interest. - However, there is a natural relaxation, roughly requiring indifference between any two opponents who are both "reasonably evenly matched" with the choosing player. We prove that this relaxed variant of opponent indifference, which we call P opponent indifference, is viable. In fact, a certain strong version of P opponent indifference precisely characterizes the rating system Sonas, which was originally proposed for its empirical predictive accuracy on the outcomes of high-level chess games. Gregory Bodwin, Forest Zhang |
ITCS | 2 |