# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 2 Related Work  
A considerable number of studies have implemented medical systems comprising wearable devices implanted with sensors to record data for health applications. Examples of the collected data include heart rate, electrocardiogram, blood pressure, sleep state, step counts, calories, etc. With intelligent systems, these lifelogging data are exploited to enable data owners to monitor individuals' health status during their daily activities, such as exercising, working, sleeping, etc. For example, CodeBlue [10] is a health care system that provides patient monitoring functionality by using body sensor data. AlarmNet [11] and Medical MoteCare [12] are patient monitoring systems that not only use body sensor data but also data gathered by environmental sensors. Health monitoring is a field that has been actively studied. The exploitation of health data makes it possible to provide personalized medical and early disease diagnosis services. For instance, a doctor examining a patient whose health data are collected in a hospital could make an assessment based on the current state of the patient in comparison with the medical records of other patients, which enables improved assessment [13]. An electrocardiogram was used to monitor the state of heart disease and predict the degree of stress by observing changes in heart rate. [PERSON] et al. [14] investigated the relationship between the stress level and changes in the heart rate. In their experiments with 28 students, the average heart rate rose from 73.52 to 75.94 when the Mensa exam was presented to the students compared to images of pastoral scenes. This means that a mentally stressful situation stimulates the heart rhythm. [PERSON] et al. [15] introduced a method whereby a wearable device can be used to track the heart rate of a patient with congestive heart failure and the condition monitored remotely. Several studies have demonstrated a significant relationship between health and exercise, indicating that those who are active in their leisure time are more likely to live longer than those who are inactive [16; 17]. With respect to human behavior recognition, recognizing patients' state by analyzing their health data is a viable option. For example, an emergency alarm system can be used to automatically request help when it detects abnormal movement of a disabled person or an elderly person with impeded mobility. These alarm systems analyze the data gathered by the inertial sensor embedded in a wearable device for monitoring purposes [18]. The analysis of daily behavior information in an office environment is a popular application in the area of human behavior recognition [19]. In this area, health data are actively being analyzed by classifying physical activities, using methods such as machine learning, and monitoring them for health management purposes. [PERSON] et al. [20] conducted a study to classify 19 types of human behavior using small inertial and magnetic sensors. Behavioral classification using health data is mainly used to provide a service for patients or the elderly. [PERSON] et al. [21] introduced a method based on electrocardiograms for detecting and preventingcollapse owing to hypotension. In another work, [PERSON] et al. [22] developed a method to provide customized healthcare services for individual users by collecting and analyzing wearable devices' biodata such as heart rates and body temperatures. Much as the works presented in [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] aim at improving the well being and the general standard of living of humans, they pay limited attention to the user privacy concerns. In [10; 11; 12; 13; 14; 15; 18; 19; 20; 21; 22], the focus is mainly on methods for collecting and analyzing user data for the purpose of monitoring, without considering the user privacy violations involved. However, in [13], though the authors considered data access security in their design, user privacy still remains a challenge once an entity gains access to the data.