A Framework for Verifiable and Auditable Collaborative Anomaly Detection

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.

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

“…It utilizes cryptographic knowledge to ensure its immutability and unforgeability. Therefore, blockchain technology prevents data tampering, and forgery, and provides flexibility in tracing data [22][23][24]. The integration of federated learning (FL) and blockchain introduces intricate computational and operational challenges, potentially amplifying the costs associated with deployment and maintenance.…”

“…Blockchain is a distributed ledger (database) shared among a computer network's nodes, used to store datasets in blocks linked together via cryptographic hashes. Blockchain is a new technology that features decentralized storage, tamper resistance, and traceability[16,17,[22][23][24].…”