Privacy preservation over federated data has gained its momentum in the era of securing users’ sensitive information. Combining and analysing sensitive information from multiple data sources offers considerable potential for knowledge discovery. However, there are different constraints which should be fulfilled, such as what are the data to be preserved; what is meant by privacy preservation; what are the constraints on federated computing; and what are the secure mechanisms to train, query and explore data without accuracy loss. We introduce the Protected Federated Query Engine which applies Fully Homomorphic Encryption and querying processing over decentralised data sources of diverse schemas and granularities to efficiently collect, align, aggregate and serve Artificial Intelligence Operations (AIOps) and Data Operations (DataOps) without sacrificing accuracy and efficiency.

Read the full publication here