The digitalization of healthcare has brought about a significant transformation in data management, presenting intricate privacy challenges due to the sensitive nature of e-healthcare information. This survey delves into robust privacy-preserving methods tailored for e-healthcare data analytics, pivotal for instilling trust in electronic healthcare systems. It examines encryption, access controls, and anonymization techniques as essential safeguards for protecting patient da-ta integrity while enabling sophisticated data analytics. Specifically, this survey critically evaluates the efficacy of Homomorphic Encryption, Secure Multi-Party Computation, Differential Privacy, and Anonymization, Federated Learning, and Block chain-based approaches in ensuring data privacy in e-healthcare contexts. By elucidating their roles in privacy preservation, this study endeavors to strike a delicate balance between the imperative of privacy and the utility of e-healthcare data for deriving valuable medical insights. The literature review section meticulously scrutinizes existing anonymity-based privacy preservation strategies in e-healthcare, elucidating key discoveries and insights. Further-more, it offers a concise overview of privacy-preserving techniques, underlining the paramount importance of privacy while maximizing the utility of healthcare data for research and operational purposes in the realm of data analytics.