## 1 Introduction  
The digitization of healthcare workflows and the increased usage of electronic health records has led to a dramatic increase in electronically available clinical data in terms of volume, complexity, diversity and timeliness. Clinical data carry information about real patients and hold the promise of supporting a wide range of unprecedented opportunities and use cases, such as clinical decision support, health insurance, disease surveillance, population health management, adverse event monitoring, and treatment optimization. Therefore, clinical data are suitable to be reused in machine learning applications, despite the fact that they can be characterized by noise, errors, incompleteness and high dimensionality [1]. Even though the digitization of clinical data in the healthcare sector carries many benefits, it also raises challenges such as the preservation of patient privacy. Health data by default are sensitive, and concerns over the compromise of sensitive information, security and privacy are increasing year by year. Security attacks can happen in the data-gathering phase, the network phase, as well as the storage phase [2]. Health Information custodians (HICs) have faced increasing privacy breaches of different types due to either the negligence of administrative staff or the employment of weak de-identification methods [3]. It has been shown that the number of health service providers reporting cases of data privacy breaches is increasing every year. In the years 2016-2017, approximately 90 percent of healthcareproviders were faced with data breaches, while a successful breach was estimated to cost around USD 3.7 million to clean up [4; 5; 6]. Furthermore, it has been shown that seemingly anonymous, de-identified data that is publicly available can be combined and linked to certain individuals or groups through other identifying attributes [7]. Data security controls the access to medical data throughout the data lifecycle, protecting it from any unauthorized third-party access, and is ensured through technologies such as authentication, encryption, data masking and access control. Data privacy, on the other hand, is concerned with the protection of an individual's healthcare information from unauthorized access. Data privacy regulates data access based on privacy policies and laws and is ensured through methods such as de-identification and anonymization [1; 8]. The most prominent data privacy model is k-anonymity [9]. Machine learning is a category of algorithmic methods enabling machines to solve problems without specific computer programming and can be distinguished by supervised, unsupervised and reinforcement methods in terms of their learning approach [8; 10]. Despite the fact that the implementation of machine learning tools for health care data faces many challenges, the use of AI in health care is surrounded with excitement, with numerous platforms integrating clinical machine learning tools having been developed [11]. Machine learning models have been utilized in several health informatics fields, such as medical imaging, medical informatics and public health [12]. For example, deep learning models have been used to predict mortality, early readmission, long length of stay, as well as future diseases, from EHRs [13; 14]. In addition, machine learning algorithms such as logistic regression, support vector machines, Gaussian naive Bayes, K-nearest neighbors and deep multilayer neural networks have been employed to predict the probability of early patient readmission from processed hospital information system (HIS) data [14]. The topic of privacy-aware machine learning, which lies at the intersection between machine learning and privacy preservation, has begun to be explored in the last few years with the advent of data privacy laws [15]. Examples of relevant research are the investigation of the impact of data anonymization on the performance of machine learning algorithms using the gradient boosting, random forest, logistic regression and linear SVC algorithms [16].