A Framework for Verifiable and Auditable Collaborative Anomaly Detection

【Author】 Santin, Gabriele; Skarbovsky, Inna; Fournier, Fabiana; Lepri, Bruno

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Collaborative and Federated Leaning are emerging approaches to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent's performance without disclosing any data. In this paper we present a novel algorithmic architecture that tackle this problem in the particular case of Anomaly Detection (or classification of rare events), a setting where typical applications often comprise data with sensible information, but where the scarcity of anomalous examples encourages collaboration. We show how Random Forests can be used as a tool for the development of accurate classifiers with an effective insight-sharing mechanism that does not break the data integrity. Moreover, we explain how the new architecture can be readily integrated in a blockchain infrastructure to ensure the verifiable and auditable execution of the algorithm. Furthermore, we discuss how this work may set the basis for a more general approach for the design of collaborative ensemble-learning methods beyond the specific task and architecture discussed in this paper.

【Keywords】Collaboration; Collaborative work; Anomaly detection; Radio frequency; Training; Computer architecture; Task analysis; Algorithm auditing; anomaly detection; blockchain; collaborative learning

【发表时间】2022

【收录时间】2022-09-06

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-网安领域