Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges

With the advent of functional encryption (FE), new possibilities for the computation of encrypted data have arisen. FE enables data owners to grant third-party access to perform specified computations without disclosing their inputs. It also provides computation results in plaintext, unlike fully homomorphic encryption (FHE). The ubiquitousness of machine learning (ML) has led to the collection of massive private data in the cloud computing environment. This raises potential privacy issues and underscores the need for more private and secure computing solutions. Numerous efforts have been made in privacy-preserving ML (PPML) to address security and privacy concerns. There are approaches based on FHE, secure multiparty computation (SMC), and, more recently, FE. Compared to FHE-based PPML techniques, FE-based PPML is still in its infancy. In this article, we provide a survey of PPML works based on FE, summarizing state-of-the-art literature. We focus on inner product-FE, function-hiding inner product encryption, and quadratic-FE-based ML models for PPML applications. We analyze the performance and usability of the available FE libraries and their applications to PPML. We also discuss future research directions for FE-based PPML approaches. To the best of our knowledge, this is the first work to survey FE-based PPML approaches.