Volume 11 - Issue 4
An Investigation of Pseudonymization Techniques in Decentralized Transactions
- Sandi Rahmadika
Wonkwang University, Jeonbuk, Iksan City 54538, Republic of Korea
ndiikaa@gmail.com
- Muhammad Firdaus
Pukyong National University, Busan 48513, Republic of Korea
mfirdaus@pukyong.ac.kr
- Yong-Hwan Lee
Wonkwang University, Jeonbuk, Iksan City 54538, Republic of Korea
hwany1458@empas.com
- Kyung-Hyune Rhee
Pukyong National University, Busan 48513, Republic of Korea
khrhee@pknu.ac.kr
Keywords: blockchain-based incentive, decentralized learning, pseudonymization protocols, smart contract
Abstract
Decentralized learning (DL) enables several devices to assemble deep learning models while keeping
their private training data on the device. Rather than uploading the training data and model
to the server, cross-silo DL only sends the local gradients gradually to the aggregation server back
and forth. Hence, DL can provide privacy training of machine learning. Nevertheless, cross-silo
DL lacks the proper incentive mechanism for the clients. Thanks to the blockchain, smart contracts
(SCs) can address the concerns by providing immutable data records which are self-executing and
tamper-proof to failures. Yet, the records of blockchain transactions are publicly visible, which can
leak valuable clients’ information as analytical systems become more sophisticated. We leverage the
Monero (XMR) protocols to be adjusted into cross-silo DL transactions over wireless networks to
address the issues. Concurrently, we investigate the performance of constructed protocols embedded
into blockchain smart contracts. This paper also reports and analyzes an empirical investigation of
several privacy preservation techniques in decentralized transactions. Overall, the performance results
satisfy the design goals. Our observations fill the current literature gap concerning an up-to-date
systematic mapping study, not to mention extensive techniques in preserving privacy for cross-silo
DL combined with blockchain.