Privacy-Preserving Techniques in Big Data Analytics
Samarth Shah, Shakeb Khan · Journal of Quantum Science and Technology. · 2024
The exponential growth of data in recent years has created unprecedented opportunities for insights through big data analytics. However, this surge in data generation also raises significant privacy concerns, especially when sensitive information is involved. Privacy-preserving techniques have emerged as a critical area of research to ensure the ethical use of data while maintaining analytical efficacy. This paper explores state-of-the-art approaches to safeguarding privacy in big data analytics. Techniques such as data anonymization, differential privacy, secure multi-party computation, and homomorphic encryption are examined for their effectiveness and applicability across various domains. Anonymization techniques aim to remove personally identifiable information while retaining analytical utility, though challenges like re-identification attacks persist. Differential privacy introduces calibrated noise to data, balancing privacy and accuracy. Secure multi-party computation enables collaborative analytics without sharing raw data, promoting confidentiality in distributed environments. Homomorphic encryption allows computations on encrypted data, ensuring security throughout the analytical process. The integration of privacy-preserving techniques into big data workflows demands addressing computational overheads, scalability, and regulatory compliance. This paper also highlights how combining multiple privacy-preserving methods can mitigate individual limitations and enhance overall robustness. As privacy regulations evolve, such as the General Data Protection Regulation (GDPR) and similar frameworks, these techniques become increasingly vital.