Data Privacy in the Digital Era: Machine Learning Solutions for Confidentiality

Abstract

Data privacy has grown to be of utmost importance in today's digitally driven world. Protecting sensitive information has never been more important due to the explosion of data across many areas. This abstract explores cutting-edge machine learning techniques for improving data privacy in the digital age.Artificial intelligence's subset of machine learning presents a viable way to overcome issues with data privacy. This study investigates how machine learning algorithms can be used to strengthen confidentiality protections in a range of applications. Machine learning models may uncover vulnerabilities and potential breaches in real time by analysing large information, offering proactive defence against cyber threats.We explore a number of data privacy topics, such as access control, encryption, and data anonymization, while emphasising how machine learning approaches might improve these procedures. We also cover how federated learning protects privacy during collaborative data analysis, enabling different parties to gain knowledge without jeopardising the integrity of the data.The importance of ethics and compliance in the creation and application of machine learning solutions for data confidentiality is also emphasised in this abstract. It highlights the necessity for ethical AI practises and highlights the difficulties in finding a balance between the preservation of privacy and the usefulness of data.This study investigates how machine learning could strengthen data confidentiality, paving the path for a more safe and considerate digital future. It highlights the value of interdisciplinary cooperation between data scientists, ethicists, and policymakers to fully utilise machine learning's promise in protecting our sensitive information in the digital world.