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Huanxi Zhang

Publications and source records attributed to Huanxi Zhang.

2 recordsLinked to original sources

When One Leak Pays Forever: Context Binding and the Price of Deterring Collusion

A coalition that deviates once can profit many times when what it sells keeps working. In a threshold-encrypted mempool, a leading defense against maximal extractable value (MEV), a quorum of the decryption committee that sells its decryption capability to a front-runner exposes every later block that the capability still decrypts. We ask how large a penalty, such as slashable stake, deters this kind of collusion. In our repeated game, a single leak by any coalition in a monotone family of authorized coalitions (for example, any $k$ of the $n$ committee members) unlocks a set of future rounds, costs a one-time penalty, and ends the coalition's participation. We show that every dynamic deviation reduces to choosing a leak time, so deterrence holds if and only if each coalition's penalty covers the largest discounted value that a single leak reaches. Without discounting, over $T$ rounds of unit value full reuse needs a penalty of $T$ while binding each leak to its own round needs $1$, so no penalty that is constant in the horizon deters unbounded reuse; a reuse window of $w$ rounds costs at most $w$ times the largest per-round value. The cheapest profile of per-party stakes that deters every coalition solves a covering linear program. For blockchain design, per-epoch keys cut the required stake from the value of a key's lifetime to the value of one epoch; we calibrate the gap on Ethereum front-running data and place Ferveo and Shutter in the model. The analysis extends to sealed-bid auctions, multi-authority voting, and federated learning under a shared key.

cs.GT↗

The Exact Welfare Guarantee of Fixed-Price Bilateral Trade

A seller and a buyer with independent private values can trade only at a posted price. We determine the worst case of this mechanism exactly: the best posted price always guarantees a $β_*=0.738024...$ fraction of first-best welfare, where $β_*$ is the root of an explicit equation, the worst-case buyer is unique up to scaling, and no pair of distributions attains the worst case. This closes the gap $[0.7292,0.73805]$ left by a line of work from SODA 2016 through two STOC 2023 papers and AAAI 2026. The proof is an explicit certificate of optimality: after one change of variables, the gap between the optimal value and that of any buyer distribution is a sum of nonnegative integrals, as in a linear-programming dual, and equality identifies the worst-case shape. The certificate also gives the complete tradeoff between gains from trade and the seller's initial welfare: when first-best gains from trade are a fraction $κ$ of initial seller welfare, the exact worst-case fraction $ρ(κ)$ of gains obtained by the best price satisfies $ρ(κ)\sim2/\log(1/κ)$ as $κ\to0$. The worst-case buyer has two constant segments joined by an explicit nonexponential curve, approached through a vanishing atom whose value tends to infinity. The same constant is the exact guarantee of dominant-strategy mechanisms with individual rationality and strong budget balance in every realization. For two units with increasing submodular valuations, an explicit finite instance has ratio below $0.7290804$, so multi-unit trade is strictly harder than single-unit trade. For any bounded ordered buyer pair we characterize the least common-price mass that guarantees a prescribed ratio against every ordered seller pair; a bound on this one-sided functional would determine the exact two-unit constant. Within an explicit family the unique minimum is $0.729080...$, conjectured to be optimal.

cs.GT↗