In the realm of cybersecurity, the preservation of privacy in data analysis processes is of paramount importance. This paper explores the application of privacy-preserving techniques, particularly focusing on the pivotal role of differential privacy. Differential privacy offers a rigorous mathematical framework to quantify privacy guarantees, ensuring that data analysis outcomes do not compromise individual privacy. Amidst escalating concerns surrounding privacy breaches and data vulnerabilities, the adoption of robust privacy-preserving measures becomes imperative. Through an extensive literature review, this paper delves into the theoretical foundations of differential privacy, evaluates its effectiveness in practical applications, and outlines future research directions. By elucidating the potential of this technique, the paper aims to contribute to the advancement of privacy-preserving practices and bolster the overall security posture in the cybersecurity landscape.