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Surv."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>The efficient and secure processing of confidential health data always remained an important challenge for healthcare professionals and policymakers as this information needs to be shared among several parties for both data analytics and improved health treatments. In this regard, Privacy Enhancing Technologies (PETs) have already shown great potential in deploying intelligent healthcare systems for improved prognosis and diagnosis. This article explains important privacy-preserving techniques by focusing on their security models and performance issues. It specifically discusses libraries and tools that can be used to implement a particular PET model. Moreover, a detailed comparison is provided to highlight the strengths and weaknesses of each of the privacy enhancing approaches. 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