In the era of big data, ensuring privacy while extracting meaningful insights has become a significant challenge for intelligent information systems. This article presents a novel approach to privacy-preserving big data analytics using fuzzy logic, offering a framework that balances data utility and privacy. By leveraging fuzzy logic’s capability to handle uncertainty and imprecision, the proposed method enhances the adaptability and interpretability of privacy mechanisms in complex data environments. The integration of fuzzy logic enables dynamic decision-making, allowing for more flexible privacy controls that align with varying data sensitivity levels. This study demonstrates how fuzzy logic-driven analytics can improve data privacy without compromising the quality of insights, thereby advancing intelligent information systems in handling sensitive information. Experimental results validate the effectiveness of the proposed approach, highlighting its potential to revolutionize privacy-preserving techniques in big data analytics.