On Learning with Confidentiality through Encrypted AI Pipelines

In the current accelerated technological ecosystem driven by advancements in Artificial Intelligence (AI) and Machine Learning (ML), there is an articulated demand for secure data operations (DataOps) and learning (AIOps or MLOps). The compromise of sensitive data can lead to irreparable harm to both individuals and organizations. Consequently, there is a critical need to adhere to various constraints designed to securely process and analyze sensitive data without revealing any pertinent information. This work introduces the Private AI/ML Operations Flow, which develops a framework able to perform descriptive and predictive analytics with confidentiality over learnt patterns. The framework utilizes Fully Homomorphic Encryption (FHE) and Order Revealing Encryption (ORE) schemes to encrypt data and trains AI/ML algorithms to learn patterns with privacy. By enabling AI/ML algorithms to be trained directly on encrypted data, the framework allows for the discovery of valuable patterns and insights without revealing the underlying sensitive information, preserving the confidentiality of the learnt patterns. The proposed methodology emphasizes maintaining both accuracy in the analytical results and efficiency in the operational processes, even with the added operation of encryption. This addresses a key concern often associated with privacy-preserving techniques, demonstrating that security does not necessarily come at the cost of performance and usability.