Models and algorithms of privacy-preserving machine learning

Abstract

The paper is devoted to the recent scientific problem of privacy-preserving machine learning. The problem actuality is determined by the growing need to use machine learning for personal data, as well as for data that make up commercial, medical, financial and other types of information protected by law. The aim of the study is to systematize the security models of machine learning, to identify algorithmic tools that can be used to ensure the privacy of the learning process and application of models, as well as to analyze the privacy-preserving machine learning systems. The paper presents the major concepts and definitions related to machine learning, provides a systematization of machine learning problems and methods of their solution, and pays attention to the modern and promising areas of development of machine learning. Among the tasks of machine learning are those for which it is important to ensure the privacy of data from training, test and work samples. Special cryptographic methods and protocols are correlated to the problems solved. A brief description of the known privacy-preserving machine learning systems is given. The machine learning methods supported by these systems, as well as the type of adversary that the system can resist, and the cryptographic primitives used for the implementation are described in the paper. Unsolved problems in the field of privacy-preserving machine learning and prospects for the development of this scientific field are considered.

Keywords

• data mining, machine learning, deep learning, federated learning, transfer learning, privacy, secure multi-party computations, secret sharing scheme, homomorphic encryption.