Citation: Jo, D.; Chung, J.-H. Design
and Application of Secret Codes for
Learning Medical Data. Appl. Sci.
2022, 12, 1709. https://doi.org/
10.3390/app12031709
Academic Editor: Hee-Cheol Kim
Received: 20 December 2021
Accepted: 5 February 2022
Published: 7 February 2022
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Article
Design and Application of Secret Codes for Learning
Medical Data
Dongsik Jo and Jin-Ho Chung *
Department of Electrical and Computer Engineering, University of Ulsan, Ulsan 44610, Korea;
dongsikjo@ulsan.ac.kr
* Correspondence: jinho@ulsan.ac.kr
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.
Keywords:
medical data; privacy; minimal code; distributed learning; federated learning; linear
block code
1. Introduction
With the fourth Industrial Revolution, the application of artificial intelligence tech-
nology is expanding in the medical field [
1
–
6
]. The biggest obstacle to the collaboration
of medical data from distinct institutes has been the protection the private information
contained in the distributed system. In particular, federate learning is in the spotlight
as a distributed machine learning technique that can simultaneously retain privacy and
efficiency [
7
]. It can produce a similar result to learning all data at once without sharing
private data. Since federated learning does not centralize data to a big server, it can pro-
tect the private information of each user. Currently, it is being applied to several areas,
including health care, smart factories, and finance [
8
–
13
]. Major companies such as Google
and NVIDIA have been conducting research on medical artificial intelligence through the
development of their own federated learning algorithms [8–10].
Linear block codes have been investigated for applications in several areas of engi-
neering, such as communication systems, cryptography, and security [
14
]. A minimal code
is a block code in which the support of a codeword is not included in that of any other
codewords [
15
]. Using the minimal code, one user’s information is not subordinate to
other users’ information. This has been constantly studied as one of the mathematical
structures that can be used in secret-sharing schemes [
16
–
23
]. Furthermore, a minimal code
can be used in federated learning due to its distributed characteristics of secret information.
Almost all the known minimal codes so far have been designed based on the structure and
characteristics of finite fields [
24
]. In particular, for binary cases, several design methods
have been proposed [
16
–
20
]. On the other hand, non-binary minimal code has been investi-
gated recently [
21
–
23
]. Research on previously known minimal codes has focused only on
the weight distribution of the codes. Considering recent applications, further characteristics
or structures of minimal codes, such as their error-correction capability and relation to the
learning rate, should be investigated.
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
Appl. Sci. 2022, 12, 1709. https://doi.org/10.3390/app12031709 https://www.mdpi.com/journal/applsci
