The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

The growth of data generation capabilities, facilitated by advancements in communication and computation technologies, as well as the rise of the Internet of Things (IoT), results in vast amounts of data that significantly enhance the performance of machine learning models. However, collecting all necessary data to train accurate models is often unfeasible due to privacy laws. Federated Learning (FL) evolved as a collaborative machine learning approach for training models without sharing private data. Unfortunately, several in-design vulnerabilities have been exposed, allowing attackers to infer private data of participants and negatively impacting the performance of the federated model. In light of these challenges and to encourage the development of FL solutions, this paper provides a comprehensive analysis of secure FL proposals that both protect user privacy and enhance the performance of the model. We performed a systematic review using predefined criteria to screen and extract data from multiple electronic databases, resulting in a final set of studies for analysis. Through the systematic review methodology, the paper groups the security vulnerabilities of FL into model-performance and data privacy attacks. It also presents an analysis and comparison of potential mitigation strategies against these attacks. Additionally, the paper conducts a security analysis of state-of-the-art FL applications and proposals based on the vulnerabilities addressed. Finally, the paper outlines the main applications of secure FL and lists future research challenges. The survey highlights the crucial role of security strategies in ensuring the protection of user privacy and model performance in the context of future FL applications.

Cross-organisation data sharing is challenging because all the involved organisations must agree on ’how’ and ’why’ the data is processed. Due to a lack of transparency, the organisations need to trust that others comply with the agreements and regulations. We propose to exploit blockchain and smart contracts technologies to define an Attribute-Based Access Control System for cross-organisation medical records sharing, coined SmartAccess. SmartAccess offers joint agreement over access policies and dynamic access control besides blockchain transparency and auditability. We leverage the Attribute-Based Access Control model to implement smart contracts.We deploy and test them on a private and permissioned blockchain, transforming the access control process into a distributed smart contract execution. This paper proposes the SmartAccess system and its application in two healthcare use cases. We introduce the threat model and perform a security analysis of the system. To demonstrate the feasibility of our proposal, we implement a proof-of-concept of the smart contracts, written in Solidity language, with a size-efficient policy representation, and analyse the complexity and scalability of the contracts’ functions. Furthermore, we present performance results, measuring the latency and throughput of the transactions to execute the access control functions with different blockchain network consensus setups. We also compare the performance of the SmartAccess system against two open-source Solidity implementations of smart contract-based access control, Role-based Access Control and Access Control List. Finally, we discuss the strengths and drawbacks of our proposal. SmartAccess requires the overhead of a decentralised system, but the trade-off is transparency, regulation compliance and auditability for complex cross-organisation data sharing.

Electronic medical records (EMRs) are highly sensitive information shared among peers to keep up-to-date patient history. Providing security, privacy, and availability to these sensitive data is a challenge because, typically, after data publication the patient loses control over them. In this paper, we propose a blockchain-based approach to secure EMR for healthcare applications, where access control is patient-centric. Our proposal keeps encrypted EMRs in the blockchain, and the patient shares the decryption key only with healthcare professionals in which he/she trusts. Blockchain allows entrusted node, in a distributed peer-to-peer network to correctly and verifiably interact with each other, without any reliable intermediary. We investigate the scalability of our approach through simulations. Results show that it scales well since increasing the number of nodes in the network implies a linear increase in the size of the stored chain. Results also reveal that the time for inserting a new EMR in the blockchain remains low even when the number of nodes in the network increases.