Blockchain technology, initially known for its applications in the financial industry, has emerged as a promising solution for various other domains. One prominent area for the use of blockchain-based solutions is forensics, specifically the chain of custody maintenance and control. While there have been numerous research projects exploring the use of blockchain technology in digital forensics, limited attention has been given to its application in controlling of the physical evidence chain of custody. In this research, we aim to explore the literature on the use of blockchain technology to solve problems related to the physical evidence chain of custody. Through a systematic literature review (SLR), we analyzed 26 resources discussing blockchain-based solutions for evidence chain of custody issues, based on requirements that could be applied to both physical and digital evidence. The results showed that there is a lack of studies involving the use of blockchain technology to solve problems related to the physical evidence chain of custody, and future research should focus on solving the issue.

ESG is a sustainability framework promoting sustainability and responsible business practices relevant to Petrobras, a major Brazilian oil and gas company. In this sense, blockchain emerged as a technology for trustworthy information storage. This work demonstrates the ESG Token solution, built on top of EVM-compatible smart contracts to address data and process transparency, security, availability, and auditability. RBB (Brazilian Blockchain Network) was used to deploy the ESG Token smart contract, and the pluggable architecture allows for separating the blockchain layer from the business solution. Furthermore, ESG Token integrates with GovBR’s digital signature platform, enabling self-declarations of validated identities to enhance accountability.

The precedent is a textual citation of prior court decisions. This undoubtedly offers great value in a common-law-based judicial system where courts are bound to their previous rulings, such as in the United States, Canada, and India. In those countries, precedent relevance detection is an issue that has attracted considerable attention where studies propose Network Science techniques for relevance measurement --- where decisions and their relationships are represented in network structures. However, those methods fail to capture the precedent relevance in the Brazilian scenario due to the massive and increasing number of decisions issued yearly. The Brazilian Supreme Court (STF), the highest judicial body in Brazil, has produced more than a million decisions over the last decade. Therefore, we propose an interpretable and cost-effective process to explore the precedent through latent topics that emerge, evolve, and fade over time in a collection of historical documents. To do so, we explore dynamic topic modeling with tensor decomposition as a tool to investigate the legal changes embedded in those decisions over time. We base our study on the individual decisions published by STF between 2000 and 2018. Additionally, through experiments, we explore the proposed process within different scenarios to investigate the precedent citations over the STF's recent history, and how those citations correlates with the legal named entities, such as legislative references. The experiments showed the process' capability to produce coherent and interpretable results for temporal analysis of the precedent citations in larger collections of documents. Also, it presents the potential to support further studies in the legal domain.

Personal data protection regulation plays an important role in defining the rights and obligations of the agents involved in processing personal data (i.e., data subjects, controllers, and processors). These agents are allowed to execute actions to achieve their goals by obeying the personal data protection rules; however, this exercise may spawn data flow information asymmetry; for instance, a company may have more information regarding how that data is being used than individuals. This asymmetry can undermine individuals’ ability to protect their rights and interests and lead to a lack of trust in organizations and government bodies responsible for protecting their data. In this context, this article proposes: (i) a consent metamodel based on the literature to aid agents in identifying their major concerns when sharing personal data; (ii) a structure to build use case scenarios in the personal data regulation context; (iii) an intelligent normative multiagent system architecture to represent the personal data regulation rights and obligations, as well as the agent’s decision-making process. The latter will consider the normative rewards and punishments in the aforementioned scenario structure; (iv) a use case in the open banking scenario. This article demonstrates how we propose to contribute to representing agents’ preferences and data regulation concerns. We do so with a normative multiagent system and designing agents with cognitive reasoning capabilities.