The rapid growth of big data analytics offers tremendous opportunities to unlock valuable insights and knowledge. However, privacy concerns have become a major barrier to exploiting the full potential of big data. This research paper explores privacy techniques designed specifically for big data analytics. This article explores the following issues of maintaining privacy, scalability, and time efficiency in the context of native re-encoded anonymization of big data in the cloud. A privacy proximity model called “-dissimilarity” has been proposed, which combines the proximity of multiple sensitive attributes and categorical sensitivity values. To solve different NP-hard problems, a proximity-sensitive integration method using MapReduce is introduced, which increases data processing capacity and efficiency. This research contributes to the field of privacy-enhancing technologies for big data analytics by paving the way for secure and ethical data analysis while protecting personal privacy.