This research paper explores the intersection of secure data mining techniques, cloud computing, and cybersecurity. As organizations increasingly rely on cloud-based systems to store and process large volumes of sensitive data, the need for robust security measures has become paramount. This study investigates novel approaches to perform data mining operations securely within cloud environments while maintaining data confidentiality, integrity, and availability. We propose a framework that combines homomorphic encryption, secure multiparty computation, and differential privacy to enable privacy-preserving data analysis in the cloud. Our experimental results demonstrate the efficacy of the proposed approach in terms of security, performance, and scalability. The findings of this study contribute to the ongoing efforts to enhance cybersecurity in cloud-based data mining applications and provide valuable insights for researchers and practitioners in the field.