Abstract
<jats:p>Real-time big data analytics has become a critical component in modern technological systems, offering significant insights into various fields such as healthcare, finance, and smart cities. However, the collection, storage, and processing of massive volumes of data present significant challenges in terms of privacy preservation. This article explores various privacy-preserving techniques that are employed in real-time big data analytics and identifies the challenges associated with their implementation. By examining encryption methods, anonymization, and differential privacy, this paper aims to shed light on the balance between data utility and privacy in the context of real-time analytics. It discusses the role of regulations like GDPR and technological advancements in addressing privacy concerns. </jats:p>