Techniques for Protecting Privacy in Big Data Security: A Comprehensive Review
Divya Joshi, Akash Sanghi, Gaurav Agarwal, Bhuvan Joshi · 2024
The rapid growth of big data and advanced analytics has unlocked immense value for organizations but has also created significant privacy risks. As companies collect and analyze ever-larger volumes of personal data, it is critical to employ privacy preserving techniques to safeguard sensitive information while still enabling big data innovation. This paper provides a comprehensive review of the latest privacy preserving methods for securing big data, including data anonymization, encryption, federated learning, differential privacy, and secure multi-party computation. We discuss the strengths and limitations of each approach and provide recommendations for their practical application based on different data security and analytics scenarios. By adopting these state-of-the-art privacy protection strategies, companies can realize the benefits of big data while preserving individual privacy and engendering trust.