Data privacy-preserving distributed knowledge discovery based on the blockchain
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Data privacy‑preserving distributed knowledge discovery based on the blockchain Keon Myung Lee1 · Ilkyeun Ra2 Accepted: 16 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Data are collected and regarded as valuable assets in many business domains. Their owner would not want to disclose them to the public due to their potential value. Distributed knowledge discovery techniques have been proposed which assume the cooperation of data owners even though they might not behave in a trustworthy manner. When a party decides to quit the cooperation in the distributed knowledge discovery, the other parties cannot continue the discovery task and hence they get some disadvantage due to the party’s betrayal. This paper is concerned with data privacy-preserving distributed knowledge discovery which gives penalty to the party who quits the cooperation in the discovery process. It proposes a blockchainbased distributed machine learning method which does not disclose the participating parties’ data and gives the penalty to betraying parties. The proposed method makes the participating parties communicate with each other via the smart contract on the blockchain network. It uses a blockchain-based incentive system to establish trust among parties and to improve the quality of discovery knowledge. The proposed method has been implemented with a smart contract on the blockchain and tested for a benchmark data. Keywords Blockchain · Smart contract · Machine learning · Knowledge · Knowledge discovery · Data privacy knowledge discovery
1 Introduction Knowledge is of importance in decision making and intelligent information services. Knowledge can be extracted from experts or extracted from data by machine learning (ML) techniques. Knowledge discovery from data have made data be recognized as valuable assets [1–3]. Data owners hence are reluctant to disclose their data to the public. When ML techniques are used to extract patterns or models, i.e., knowledge, it is better to use as many data as possible. When data are collected for a specific domain by multiple parties, it would be better to use all their data for knowledge discovery. If there are no trust relationships established among * Keon Myung Lee [email protected] Ilkyeun Ra [email protected] 1
Department of Computer Science, Chungbuk National University, Cheongju, Korea
Department of Computer Science and Engineering, University of Colarado Denver, Denver, USA
2
the parties, such sharing is not amenable. We are interested in a method to extract knowledge from distributed data while preserving data privacy [4]. There have been various approaches to privacy-preserving distributed knowledge discovery [1–4]. It is concerned with such a method not to make data owners break the trust. Blockchain is a mechanism to provide a public ledger service with which once some pieces of information are registered, they persist theoretically forever [5, 6]. It allows cryptocurrency coins to be transferred from one account to another, tra
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