Informatica

Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence.

Voting systems are as useful as people are willing to use them. Although many electronic election schemes have been proposed through the years, and some real case scenarios have been tested, people still do not trust electronic voting. Voting is not only about technological challenges but also about credibility, therefore, we propose a voting system focused on trust. We introduce political parties as active partners in the elections as a mechanism to encourage more traditional electors to participate. The system we propose here preserves elector’s privacy, it operates publicly through a blockchain and it is auditable by third parties.

The article presents the tax declaration scheme using blockchain confidential transactions based on the modified ElGamal encryption providing additively-homomorphic property. Transactions are based on the unspent transactions output (UTxO) paradigm allowing to effectively represent digital asset of cryptocurrencies in e-wallets and to perform financial operations. The main actors around transaction are specified, include money senders, receivers, transaction creator, Audit Authority (AA) and Net of users. A general transaction model with M inputs and N outputs is created, providing transaction amount confidentiality and verifiability for all actors with different levels of available information.

Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment. Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years. It is meaningful to reveal the development level and trends based on this literature. This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country. Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.