Cloud-based Big Data Mining and Machine Learning: Ensuring Privacy and Security in Automated Systems

While the arrival of cloud computing, big data mining and machine learning has completely changed numerous sectors, creating an array of new possibilities for analyzing huge amounts of data in decision-making process. This integration however also comes with serious privacy and security issues. Cloud Based Platforms: Cloud based platforms enable the storage and processing of huge data sets which makes possible for Data Mining and Machine Learning algorithms to discover insights that are valuable. However, safeguarding sensitive information still poses a significant challenge. Privacy issues are due to an unauthorized use of users' personal information and the abuse of privacy, while security threats lie in data breaches or cyber-attacks. Abstract: We study at this intersection, and investigate how cloud-based big data mining/machine learning can be complemented with robust IS-backed privacy as well as consumer-specific security measurements. This now looks at higher-level strategies and technologies, such as encryption, access controls, privacy-preserving algorithms that can be used to protect data in automated systems. Homomorphic encryption and secure multi-party computation are two common methods to use while working with encrypted data as it goes along ways in terms of keeping the underlying information safe. As for the privacy, access control mechanisms ensure that unauthorized persons cannot reach sensitive data and approaches like differential privacy or federated learning allow to learn from a large pool of different individuals without disclosing any particular information about them. The abstract also mentions the importance of regulatory frameworks and standards to establish best practices, comply with data protection legislation. This enables enterprises to leverage the wealth of insights available through cloud-based big data analysis and machine learning with significantly reduced exposure to privacy and security risks. That balance is important for keeping the trust of users and maximizing the value derived from automated systems. The research illustrates the importance of continued innovation to meet new threats and obstacles, and emphasizes that cloud technologies will continue down a security-privacy path as they mature.