Visible to the public Biblio

Filters: Author is Markelon, Sam A.  [Clear All Filters]
2023-02-03
Markelon, Sam A., True, John.  2022.  The DecCert PKI: A Solution to Decentralized Identity Attestation and Zooko’s Triangle. 2022 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS). :74–82.
We propose DecCert, a decentralized public key infrastructure designed as a smart contract that solves the problem of identity attestation on public blockchains. Our system allows an individual to bind an identity to a public blockchain address. Once a claim of identity is made by an individual, other users can choose to verify the attested identity based on the evidence presented by an identity claim maker by staking cryptocurrency in the DecCert smart contract. Increasing levels of trust are naturally built based upon the amount staked and the duration the collateral is staked for. This mechanism replaces the usual utilization of digital signatures in a traditional hierarchical certificate authority model or the web of trust model to form a publicly verifiable decentralized stake of trust model. We also present a novel solution to the certificate revocation problem and implement our solution on the Ethereum blockchain. Further, we show that our design solves Zooko’s triangle as defined for public key infrastructure deployments.
2019-10-08
Krawec, Walter O., Markelon, Sam A..  2018.  Genetic Algorithm to Study Practical Quantum Adversaries. Proceedings of the Genetic and Evolutionary Computation Conference. :1270–1277.

In this paper we show how genetic algorithms can be effectively applied to study the security of arbitrary quantum key distribution (QKD) protocols when faced with adversaries limited to current-day technology. We compare two approaches, both of which take into account practical limitations on the quantum power of an adversary (which can be specified by the user). Our system can be used to determine upper-bounds on noise tolerances of novel QKD protocols in this scenario, thus making it a useful tool for researchers. We compare our algorithm's results with current known numerical results, and also evaluate it on newer, more complex, protocols where no results are currently known.