Privacy Preservation in the Age of Big Data: Insights from Information Theory

Abstract

Privacy preservation in the age of big data presents a multifaceted challenge, demanding innovative approaches grounded in information theory. As vast amounts of personal data are collected and analyzed, ensuring the confidentiality and integrity of sensitive information becomes paramount. Information theory offers valuable insights by quantifying the amount of information leaked during data processing and transmission, enabling the development of robust privacy-preserving mechanisms. Techniques such as differential privacy, homomorphic encryption, and secure multiparty computation emerge as promising solutions, leveraging mathematical principles to safeguard privacy without sacrificing utility. By embracing the principles of information theory, stakeholders can navigate the complexities of big data while upholding individuals' right to privacy in an increasingly data-driven world.