https://www.selleckchem.com/products/ly3039478.html Due to the advent of novel technologies and digital opportunities allowing to simplify user lives, healthcare is increasingly evolving towards digitalization. This represent a great opportunity on one side but it also exposes healthcare organizations to multiple threats (both digital and not) that may lead an attacker to compromise the security of medial processes and potentially patients' safety. Today technical cybersecurity countermeasures are used to protect the confidentiality, integrity and availability of data and information systems - especially in the healthcare domain. This paper will report on the current state of the art about cyber security in the Healthcare domain with particular emphasis on current threats and methodologies to analyze and manage them. In addition, it will introduce a multi-layer attack model providing a new perspective for attack and threat identification and analysis.Passive health monitoring has been introduced as a solution for continuous diagnosis and tracking of subjects' condition with minimal effort. This is partially achieved by the technology of passive audio recording although it poses major audio privacy issues for subjects. Existing methods are limited to controlled recording environments and their prediction is significantly influenced by background noises. Meanwhile, they are too compute-intensive to be continuously running on smart phones. In this paper, we implement an efficient and robust audio privacy preserving method that profiles the background audio to focus only on audio activities detected during recording for performance improvement, and to adapt to the noise for more accurate speech segmentation. We analyze the performance of our method using audio data collected by a smart watch in lab noisy settings. Our obfuscation results show a low false positive rate of 20% with a 92% true positive rate by adapting to the recording noise level. We also reduced model me