Personalization involves tailoring services or content to individual users based on their preferences, behaviors, and characteristics with machine learning (ML). Balancing personalization with privacy is a critical challenge. Private Information Retrieval (PIR) protocols allow users to access specific data from a server without revealing the details of their queries, which addresses concerns associated with confidentiality in information retrieval scenarios. Homomorphic Encryption (HE), on the other hand, enables computations on encrypted data, preserving the privacy of sensitive information during processing. PIR and HE can be integrated into recommendation systems powered by ML models to enable personalized services while preserving the confidentiality of user data. This intersection provides a privacy-enhanced approach to deliver customized experiences in various domains.

Practical applications and recent advancements in PIR and HE have shown considerable promise in addressing privacy concerns for personalization, but to date these methods have have limited exposure in the in ML community.

This tutorial aims to explore the synergies between Personalization, PIR, and HE to create a comprehensive framework for developing privacy-preserving personalized services that could benefit a broad audience that are interested in the theoretical and/or the practical aspects of privacy-preserving machine learning (PPML) with HE.