# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
###### Abstract  
The popularity of wearable devices equipped with a variety of sensors that can measure users' health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual's health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer's personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users' wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach. health data; wearable device; data privacy; health data aggregation +
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The health data collected from wearable devices are useful for the provision of smart healthcare services; however, the indiscriminate collection of individuals' health data may raise serious privacy concerns because of the personal and sensitive nature of information pertaining to a person's health and lifestyle. For example, a user's private lifestyle could be inferred by analyzing the change in their daily cumulative step-count. From Figure 1, the activities associated with a user's lifestyle, such as the time they left home for work and the time they spent working in the office, can be deduced based on the change in the daily cumulative step-count as a function of time. The periods with little change in the step-count denote periods when the user is either remaining in a specific location or moving by using transportation vehicles, whereas periods during which the step-count rapidly increases denote periods when they are walking or exercising. Furthermore, combined with external data, such as information about the user's location measured by smartphone, an accurate impression of the user's lifestyle is feasible. Therefore, users are generally reluctant to provide their health-related data, measured by wearable devices, to service providers owing to privacy concerns. These concerns have been identified as the most significant challenge faced by smart healthcare services. The literature on data management contains reports of extensive studies relating to data privacy protection. From the literature, differential privacy (DP) has emerged as a de facto standard for privacy-preserving data processing. DP is based on a formal mathematical definition that provides a probabilistic privacy guarantee against attackers with arbitrary background knowledge [1; 2]. In a DP-based data collection scenario, a centralized trusted data aggregator (who is trusted to not reveal the original data received from data owners) collects the original data from the data owners, perturbs the collected original data by adding random noise to satisfy DP, and publishes the perturbed data for the purpose of data analysis.