Change is the new norm for the global healthcare sector. In fact, digitization of health and patient data is undergoing a dramatic and fundamental shift in the clinical, operating and business models and generally in the world of economy for the foreseeable future. Tis shift is being spurred by aging populations and lifestyle changes; the proliferation of software applications and mobile devices; innovative treatments; heightened focus on care quality and value; and evidence-based medicine as opposed to subjective clinical decisions—all of which are leading to ofer signifcant opportunities for supporting clinical decision, improving healthcare delivery, management and policy making, surveilling disease, monitoring adverse events, and optimizing treatment for diseases afecting multiple organ systems [1, 2]. As noted above, big data analytics in healthcare carries many benefts, promises and presents great potential for transforming healthcare, yet it raises manifold barriers and challenges. Indeed, the concerns over the big healthcare data security and privacy are increased year-by-year. Additionally, healthcare organizations found that a reactive, bottom-up, technology-centric approach to determining security and privacy requirements is not adequate to protect the organization and its patients [3]. Motivated thus, new information systems and approaches are needed to prevent breaches of sensitive information and other types of security incidents so as to make efective use of the big healthcare data. In this paper, we discuss some interesting related works and present risks to the big health data security as well as some newer technologies to redress these risks. Ten, we focus on the big data privacy issue in healthcare, by mentioning various laws and regulations established by diferent regulatory bodies and pointing out some feasible techniques used to ensure the patient’s privacy. Tereafter, we provide some proposed techniques and approaches that were reported in the literature to deal with security and privacy risks in healthcare while identifying their limitations. Lastly, we ofer conclusions and highlight the future directions.