A Framework for Verifiable and Auditable Collaborative Anomaly Detection

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

Complex event processing over streaming multi-cloud platforms - The FERARI approach. Demo

We present FERARI, a prototype for processing voluminous event streams over multi-cloud platforms. At its core, FERARI both exploits the potential for in-situ (intra-cloud) processing and orchestrates inter-cloud complex event detection in a communication-efficient way. At the application level, it includes a user-friendly query authoring tool and an analytics dashboard providing granular reports about detected events. In that, FERARI constitutes, to our knowledge, the first complete end-to-end solution of its kind. In this demo, we apply the FERARI approach on a real scenario from the telecommunication domain