Developments in sensing and analysis methods have significantly increased the scope of physiological monitoring for healthcare purposes. While the continuous monitoring of physiological measurements enables improved detection and management of many illnesses, accompanying cybersecurity concerns continue to evolve. The large amounts of individualized data necessary to enable learned models for analysis must be sufficiently protected. In addition, the analysis and classification methods themselves should not be vulnerable to attack. This work addresses adversarial individual identification with multiple forms of physiological data, as well as potential performance interruption attacks. The paper proposes a homomorphic encryption scheme to mitigate both of these threats.