We find that recent al-gorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rig-orously validated subsets of the benchmark data.

We introduce the notion of strong secret sharing with snitching, which models the existence of a (public-state) trusted arbiter accessible to the colluders and can compensate the shareholder that the judge punished.

We find that recent algorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rigorously validated...

Such a “snitching proof” can be sent to a smart contract (modeled as a “judge”) deployed on the blockchain, which punishes the misbehaving party financially. In this paper, we extend the results from the work of CCS’24 by addressing its two main shortcomings.

Within this model, we introduce a novel primitive called secret sharing with snitching (SSS), in which each attempt to illegally reconstruct the shared secret S results in a proof that can be used to prove such misbehavior (and, e.g., financially penalize the cheater on a blockchain).

Work through problems step by step with the AI tutor. Turn dense chapters into clean notes, diagrams included.

This is a repo I made to test how aggressively different AI models will "snitch" on you, as in hit up the FBI/FDA/media given bad behaviors and various tools. Learn more in my YouTube video all about SnitchBench and the reasons why I made it:

We find that recent algorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rigorously validated subsets of the benchmark data.

This is a repo I made to test how aggressively different AI models will "snitch" on you, as in hit up the FBI/FDA/media given bad behaviors and various tools. Full YouTube video breakdown coming soon. I included a JSON breakdown here if you want to see.

Such a “snitching proof” can be sent to a smart contract (modeled as a “judge”) deployed on the blockchain, which punishes the misbehaving party financially. In this paper, we extend the results from the work of CCS’24 by addressing its two main shortcomings.