基于机器学习和区块链的大数据到数据中间平台的数据安全与隐私保护框架

Chuqiao Chen, S. B. Goyal
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引用次数: 2

摘要

利用物联网收集调制解调器数据,存储在分布式云存储中,发布用于数据挖掘或训练人工智能。这些新的数字技术融合到数据中间平台中,促进了工业的进步,推动了第四次工业革命。同时也带来了安全和隐私保护方面的挑战。隐私数据泄露可能发生在大数据生命周期的任何阶段,数据中间平台也面临着类似的情况。如何使隐私避免泄漏是刻不容缓的。传统的隐私保护模式是不够的,我们需要机器学习和区块链的帮助。在本研究中,研究者首先回顾了大数据、机器学习、区块链等相关工作中的安全与隐私防范。然后找到理论和实际工作之间的差距。基于这些差距,尝试创建一个合适的框架来指导行业在组织贡献和运营其数据中间平台时保护其隐私。不仅是学者,而且行业从业者特别是中小企业也将从这项研究中受益。
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Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0
The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence. These new digital technologies integrate into the data middle platform have facilitated the progress of industry, promoted the fourth industrial revolution. And it also has caused challenges in security and privacy-preventing. The privacy data breach can happen in any phase of the Big-Data life cycle, and the Data Middle Platform also faces similar situations. How to make the privacy avoid leakage is exigency. The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain. In this research, the researcher reviews the security and privacy-preventing in Big-Data, Machine Learning, Blockchain, and other related works at first. And then finding some gaps between the theory and the actual work. Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform. No only academicians, but also industry practitioners especially SMEs will get the benefit from this research.
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