Design and Application of Secret Codes for Learning Medical Data

Abstract

In distributed learning for data requiring privacy preservation, such as medical data, the distribution of secret information is an important problem. In this paper, we propose a framework for secret codes in application to distributed systems. Then, we provide new methods to construct such codes using the synthesis or decomposition of previously known minimal codes. The numerical results show that new constructions can generate codes with more flexible parameters than original constructions in the sense of the number of possible weights and the range of weights. Thus, the secret codes from new constructions may be applied to more general situations or environments in distributed systems.