PRIVACY-PRESERVING TECHNIQUES IN AI-DRIVEN BIG DATA CYBER SECURITY FOR CLOUD

Abstract

Artificial intelligence (AI) and big data technologies are evolving at a rapid pace, completely changing the cybersecurity landscape in cloud environments. But this advancement also presents previously unheard-of difficulties, namely with regard to privacy issues when managing enormous volumes of sensitive data. This abstract investigates privacy-preserving methods in the context of cloud-based AI-driven big data cybersecurity. Strong privacy safeguards are essential as more and more businesses rely on cloud services to handle and keep their data. This study explores cutting-edge techniques and tools designed to protect user privacy while utilising AI to analyses large amounts of data for cybersecurity purposes. Thanks to digital technology, a wide range of organizations, including banks, supply chains, e-commerce, healthcare, and retail, are producing enormous amounts of data. Machines and people both add to data through internet-based records, shut circuit TV streaming, and different means. Online entertainment and cell phones make tremendous measures of data consistently. To aid in direction, the monstrous measures of data delivered by the various sources can be handled and analyzed. Data examination, in any case, is defenseless against privacy encroachment. Suggestion frameworks, which are habitually utilized by web-based business destinations like Amazon and Flipkart to propose things to clients based on their buying propensities, are one utilization of data examination that could bring about deduction assaults.