Preserving Privacy in Big Data Analytics: A Differential Privacy Approach in Cyber Physical System

The burgeoning growth of Big Data in cyber-physical systems (CPS) has precipitated an imperative for robust privacy-preserving mechanisms. This paper introduces a novel framework for big data analytics within CPS that employs differential privacy as its cornerstone. Differential privacy provides a quantifiable approach to ensure that the privacy of individual data entries is protected while still permitting the aggregate data to be analyzed. By integrating this methodology into CPS, the proposed framework addresses the dichotomy of data utility and privacy. The research delineates the application of differential privacy techniques to a variety of data mining tasks specific to CPS, such as real-time monitoring and predictive maintenance, while maintaining the fidelity of data analysis. Furthermore, the framework is evaluated against several metrics that reflect the privacy-utility trade-off, demonstrating that it significantly mitigates the risk of privacy breaches. The adaptability of the approach is showcased through its application in diverse CPS scenarios, emphasizing its potential for widespread adoption. This paper advances the discourse on privacy in big data analytics by presenting a solution that balances the competing needs of data privacy and utility, ensuring that CPS can leverage the full potential of big data without compromising individual privacy.