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Michael Crystal

Publications and source records attributed to Michael Crystal.

3 recordsLinked to original sources

Finding a Crab in the C: Assured Translation via Comparative Symbolic Execution

Modern high-assurance software systems development favors memory safe languages such as SPARK (ADA) or Rust. However, developers often encounter non-memory safe code (e.g., C) in legacy systems and libraries which would be prohibitively expensive or risky to re-write. In response, developers have begun turning to machine learning/AI systems and other automated code translators. Automated translation comes with its own risks, however. The original and ported code are not precisely the same, semantically - otherwise there would be no point in performing the translation. To reduce these risks, we have developed cozy, a comparative binary analysis tool that simultaneously analyzes a binary compiled from "unsafe" source code and a binary compiled from a translation of the source code to a memory safe language. cozy walks the developer through differences in the behavior of the two binaries, presenting each difference and asking the user to assess whether the difference is intentional (good) or erroneous. Outside of the flagged differences, the binaries are formally verified to be equivalent. Consequently, the review process guarantees equivalence modulo changes approved by the developer. cozy has applications to automated translation, bug correction, code reviews, operation authorization, and automatic translation.

cs.SE

Rationally Analyzing Shelby: Proving Incentive Compatibility in a Decentralized Storage Network

Decentralized storage is one of the most natural applications built on blockchains and a central component of the Web3 ecosystem. Yet despite a decade of active development -- from IPFS and Filecoin to more recent entrants -- most of these storage protocols have received limited formal analysis of their incentive properties. Claims of incentive compatibility are sometimes made, but rarely proven. This gap matters: without well-designed incentives, a system may distribute storage but fail to truly decentralize it. We analyze Shelby -- a storage network protocol recently proposed by Aptos Labs and Jump Crypto -- and provide the first formal proof of its incentive properties. Our game-theoretic model shows that while off-chain audits alone collapse to universal shirking, Shelby's combination of peer audits with occasional on-chain verification yields incentive compatibility under natural parameter settings. We also examine coalition behavior and outline a simple modification that strengthens the protocol's collusion-resilience.

cs.GT

The Near Miss Effect and the Framing of Lotteries

We present a framework for analyzing the near miss effect in lotteries. A decision maker (DM) facing a lottery, falsely interprets losing outcomes that are close to winning ones, as a sign that success is within reach. As a result of this false belief, the DM will prefer lotteries that induce a higher frequency of near misses, even if the underlying probability of winning is constant. We define a near miss index that measures the near miss effect induced by a given lottery and analyze the optimal lottery design in terms of near miss. This analysis leads us to establish a fruitful connection between our near miss framework and the field of coding theory. Building on this connection we compare different lottery frames and the near miss effect they induce. Analyzing an interaction between a seller and a buyer of lotteries allows us to gain further insight into the optimal framing of lotteries and might offer a potential explanation as to why lotteries with a very small probability of winning are commonplace and attractive.

econ.TH