Comparativa de pruebas de conocimiento cero en Iota y Ethereum
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2024
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Abstract
Las pruebas de conocimiento cero (ZKP) han aparecido, gracias a la tecnología Blockchain, como una solución para garantizar privacidad y seguridad en sistemas descentralizados. Esta solución es particularmente interesante en Ethereum e IOTA, dos sistemas descentralizados de distinta naturaleza, en su búsqueda por equilibrar la descentralización, la seguridad y la eficiencia en entornos de alto rendimiento. Los ZKP en Ethereum e IOTA se utilizan para aumentar la privacidad de las transacciones, permitiendo su verificación sin revelar información sensible. Este trabajo analiza el rendimiento de tres tipos de ZKP -Groth16, Plonk y STARKimplementados en las plataformas Ethereum e IOTA. Se hace énfasis en variables clave como son el tiempo de generación y verificación de pruebas, el tamaño de las pruebas y el consumo de gas. Los resultados obtenidos indican que Ethereum, a pesar de ser un sistema más robusto en términos de seguridad y descentralizado, sufre de un mayor consumo de gas en comparación con IOTA. En cuanto a los tiempos de verificación, se comprueba que son más elevados en IOTA, aunque se muestran más constantes y dependen en mayor medida del algoritmo de ZKP utilizado y de la manera en la que se configura el entorno de pruebas. Los resultados obtenidos inciden en la importancia de elegir correctamente la plataforma
y el algoritmo de ZKP según el contexto.
Zero-knowledge proofs (ZKP) have appeared, thanks to Blockchain technology, as a solution to ensure privacy and security in decentralized systems. This solution is particularly interesting in Ethereum and IOTA, two decentralized systems of different nature, in their quest to balance decentralization, security and efficiency in high-performance environments. ZKPs in Ethereum and IOTA are used to increase the privacy of transactions, allowing their verification without revealing sensitive information. This paper analyzes the performance of three types of ZKPs-Groth16, Plonk, and STARK-implemented on Ethereum and IOTA platforms. Emphasis is placed on key variables such as proof generation and verification time, proof size, and gas consumption. The results obtained indicate that Ethereum, despite being a more robust system in terms of security and decentralized, suffers from higher gas consumption compared to IOTA. As for verification times, it is found that they are higher in IOTA, although they are shown to be more constant and depend to a greater extent on the ZKP algorithm used and the way the testing environment is configured. The results obtained emphasize the importance of choosing the right platform and ZKP algorithm according to the context.
Zero-knowledge proofs (ZKP) have appeared, thanks to Blockchain technology, as a solution to ensure privacy and security in decentralized systems. This solution is particularly interesting in Ethereum and IOTA, two decentralized systems of different nature, in their quest to balance decentralization, security and efficiency in high-performance environments. ZKPs in Ethereum and IOTA are used to increase the privacy of transactions, allowing their verification without revealing sensitive information. This paper analyzes the performance of three types of ZKPs-Groth16, Plonk, and STARK-implemented on Ethereum and IOTA platforms. Emphasis is placed on key variables such as proof generation and verification time, proof size, and gas consumption. The results obtained indicate that Ethereum, despite being a more robust system in terms of security and decentralized, suffers from higher gas consumption compared to IOTA. As for verification times, it is found that they are higher in IOTA, although they are shown to be more constant and depend to a greater extent on the ZKP algorithm used and the way the testing environment is configured. The results obtained emphasize the importance of choosing the right platform and ZKP algorithm according to the context.
Description
Trabajo de Fin de Máster en Internet de las Cosas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2023/2024.
El código desarrollado se encuentra en dos repositorios de Github https://github.com/SergioGarridoDeCastro/codigoZKP_TFM y https://github.com/SergioGarridoDeCastro/codigoStark_TFM