Data mining has become an essential tool in various domains, including healthcare, for finding patterns and relationships in large datasets to solve business issues. However, given the sensitivity of healthcare data, safeguarding confidentiality and privacy to protect patient information is highly prioritized. This literature review focuses on security and privacy methods used in data mining within the healthcare field. The study examines various techniques employed to secure and preserve the privacy of healthcare data and explores their applications. The review addresses research questions about security and privacy techniques in healthcare data mining and their specific use cases. By summarizing the current state of security and privacy methods, this review aims to contribute to the knowledge base of data mining in healthcare and provide insights for future research. The results show that anonymization, cryptography, blockchain, differential privacy, and randomization techniques are the most prevalent methods. However, more research is needed to provide sufficiently secure methods that still preserve the data's utility.