Abstract
In an era of increasing reliance on data-driven insights, the need to protect the pursuit of knowledge and privacy has become even more important. This research paper reflects on the field of privacy-preserving data analytics, and data obfuscation techniques play an important role in achieving this delicate balance. We present a comprehensive overview of known data perturbation techniques like Randomized Response, Homomorphic Encryption and Secure Multi-Party Computation, each designed to obfuscate sensitive data while facilitating meaningful analysis. A comparative analysis highlights the inherent advantages and disadvantages of these privacy protection methods, considering factors such as the level of privacy protection, ease of implementation, impact on data accuracy, and scale.