Towards a Privacy Compliant Cloud Architecture for Natural Language Processing Platforms

Abstract

Natural language processing in combination with advances in artificial intelligence is on the rise. However, compliance constraints while handling personal data in many types of documents hinder various application scenarios. We describe the challenges of working with personal and particularly sensitive data in practice with three different use cases. We present the anonymization bootstrap challenge in creating a prototype in a cloud environment. Finally, we outline an architecture for privacy compliant AI cloud applications and an anonymization tool. With these preliminary results, we describe future work in bridging privacy and AI.