Privacy-centric software design for scalable big data analytics
Deepika Dubey, Devanshu Tiwari, Atharva Jaiswal · 2025
The advent of big data has catalyzed industry transformations by enabling comprehensive analysis of vast datasets. However, this capability comes with significant privacy concerns. This chapter delves into software design strategies aimed at ensuring efficient privacy preservation in big data environments. Key techniques such as federated learning, homomorphic encryption, and differential privacy are examined for their ability to protect sensitive information without compromising data utility. The challenges associated with the variety, sheer volume, and velocity of big data are addressed, along with architectural solutions that balance performance and privacy. We explore the implementation of privacy-preserving algorithms and protocols, emphasizing the need for scalability and robustness in real-world applications. Case studies illustrate the practical application of these methodologies, highlighting their effectiveness in safeguarding data privacy while maintaining analytical accuracy. By adopting these advanced software design principles, organizations can achieve regulatory compliance, build user trust, and leverage big data responsibly and ethically.