  With the advent of functional encryption, new possibilities for computation
on encrypted data have arisen. Functional Encryption enables data owners to
grant third-party access to perform specified computations without disclosing
their inputs. It also provides computation results in plain, unlike Fully
Homomorphic Encryption. The ubiquitousness of machine learning has led to the
collection of massive private data in the cloud computing environment. This
raises potential privacy issues and the need for more private and secure
computing solutions. Numerous efforts have been made in privacy-preserving
machine learning (PPML) to address security and privacy concerns. There are
approaches based on fully homomorphic encryption (FHE), secure multiparty
computation (SMC), and, more recently, functional encryption (FE). However,
FE-based PPML is still in its infancy and has not yet gotten much attention
compared to FHE-based PPML approaches. In this paper, we provide a
systematization of PPML works based on FE summarizing state-of-the-art in the
literature. We focus on Inner-product-FE and Quadratic-FE-based machine
learning models for the PPML applications. We analyze the performance and
usability of the available FE libraries and their applications to PPML. We also
discuss potential directions for FE-based PPML approaches. To the best of our
knowledge, this is the first work to systematize FE-based PPML approaches.
