A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field's ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study's findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97.11% and an average federated learning accuracy of 93.95%.

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment.

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

The research concerns data collected in independent sets-more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent classifiers can be found, but insufficient efforts have been made on the study of tables' cooperation and coalitions' formation. The importance of such an approach was expected on two levels. First, the impact on the quality of classification-the ability to build combined classifiers for coalitions of tables should allow for the learning of more generalized concepts. In turn, this should have an impact on the quality of classification of new objects. Second, combining tables into coalitions will result in reduced computational complexity-a reduced number of classifiers will be built. The paper proposes a new method for creating coalitions of local tables and generating an aggregated classifier for each coalition. Coalitions are generated by determining certain characteristics of attribute values occurring in local tables and applying the Pawlak conflict analysis model. In the study, the classification and regression trees with Gini index are built based on the aggregated table for one coalition. The system bears a hierarchical structure, as in the next stage the decisions generated by the classifiers for coalitions are aggregated using majority voting. The classification quality of the proposed system was compared with an approach that does not use local data cooperation and coalition creation. The structure of the system is parallel and decision trees are built independently for local tables. In the paper, it was shown that the proposed approach provides a significant improvement in classification quality and execution time. The Wilcoxon test confirmed that differences in accuracy rate of the results obtained for the proposed method and results obtained without coalitions are significant, with a p level = 0.005. The average accuracy rate values obtained for the proposed approach and the approach without coalitions are, respectively: 0.847 and 0.812; so the difference is quite large. Moreover, the algorithm implementing the proposed approach performed up to 21-times faster than the algorithm implementing the approach without using coalitions.