Privacy-Preserving Data Engineering: Techniques, Challenges, and Future Directions
Sainath Muvva · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
This study investigates privacy-enhancing data manipulation techniques, exploring methods that safeguard confidential information while enabling effective data analysis. We examine a range of approaches, including data obfuscation, epsilon-differential privacy, and secure multiparty computation, while also addressing the challenges organizations face in implementing these strategies. The paper highlights emerging technologies like homomorphic encryption and federated learning, which promise to revolutionize secure data collaboration. By exploring the delicate balance between data utility and privacy protection, we offer insights into maximizing analytical value while minimizing privacy risks. The study concludes by proposing future research directions to advance privacy-centric data practices in an evolving regulatory landscape.