Data Privacy using Block Chain and AI
G.Kiran Kumar, A.Hari Prasad, B. Balaraju, Nurussobah Hussain, B. Guna Sai Reddy, M. Jaya Kishore · Zenodo (CERN European Organization for Nuclear Research) · 2023
While data is the fuel that drives AI algorithms, it is difficult to approve or authenticate its use in the complex internet where it resides because of its dispersed nature and the fact that its diverse stakeholders do not trust one another's stewardship. Due to this, it is challenging to facilitate data exchange in cyberspace for true big data and true powerful AI. In this paper, we propose the SecNet, an architecture that integrates three key components to enable secure data storage, computing, and sharing in the large-scale Internet environment, with the goal of creating a safer online environment rich in authentic big data and, by extension, a more robust artificial intelligence thanks to a larger pool of relevant information from which to draw. 1) Blockchain-based data sharing with ownership guarantee, allowing trustworthy data sharing in the large-scale environment to produce genuine big data. 2) An AI- based safe computing platform that may generate smarter security rules and so contribute to the development of a more reliable digital environment. As a result, greater AI performance may be attained by promoting data sharing and using a trusted value-exchange system for buying security services, which gives participants a chance to earn monetary benefits for supplying their data or service. In addition, we cover the usual deployment of SecNet and its applications.