UNLIKE the traditional usage models for embedded systems security, nowadays, emerging computing systems are embedded in every aspect of human lives. One of the emerging usage models in which security is vital is deeplyembedded computing systems in human bodies, e.g., implantable and wearable medical devices. In addition to the security threats to traditional embedded systems, emerging deeply-embedded computing systems exhibit a larger attack surface, prone to more serious or life-threatening attacks. Biomedical deeply-embedded systems (deployed in human body, with computer programs sending and receiving medical data and performing data mining for the decisions) are currently getting developed with rapid rate and tremendous success. Moreover, the security/privacy issues in every aspect of bioinformatics (algorithmic, statistical, and the like) including secure and private big data analytics, acquisition, and storage, privacy-preserving data mining for biomedicine, secure machine-learning of bioinformatics, and security of hardware and software systems used for biological databases are emerging given their unique constraints. Many of the systems for such computations will need to be transparently integrated into sensitive environments --- the consequent size and energy constraints imposed on any security solutions are extreme. Thus, unique challenges arise due to the sensitivity of computation processing, need for security in implementations, and assurance gaps.