Local differential privacy protection for wearable device data

Abstract

Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users’ personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved.

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

“…RQF1 distinctly highlights the security measure that could arise from the extensive use of AI and ML in healthcare. In this context, we have pinpointed a range of security measures, varying from basic [ 3 , 23 , 24 , 77 , 84 , 85 , 95 , 96 , 97 , 98 , 99 , 100 , 101 ] to advanced [ 62 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 ], which can be effectively implemented to ensure the secure usage and management of PGHD. RQF2 [ 6 , 21 , 23 , 24 , 25 , 26 , 27 , 37 , 53 , 58 , 73 , 77 , 78 , 79 , 81 , 85 , 95 , 112 ] suggests that the development of wearable devices and internet-connected medical devices over the next decade will profoundly impact PGHD security and privacy.…”

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

“…Furthermore, due to the wearable technology industry's rapid expansion, there is a lack of industry-wide standards for the transmission and encryption of health data. 121 , 122 , 124 , 125 , 126 The inadequacies of security measures may result in the exposure of private health data, potentially damaging the trust essential to the doctor–patient relationship. 125 , 126 Moreover, since the private sector is responsible for a majority of SmartWear innovation, providers have raised concern over inadequate regulatory oversight to ensure that health data is being used solely for the benefit of patients and not corporate profits.…”

“… 121 , 122 , 124 , 125 , 126 The inadequacies of security measures may result in the exposure of private health data, potentially damaging the trust essential to the doctor–patient relationship. 125 , 126 Moreover, since the private sector is responsible for a majority of SmartWear innovation, providers have raised concern over inadequate regulatory oversight to ensure that health data is being used solely for the benefit of patients and not corporate profits. 122 , 125 , 127 These concerns have already been realized with pharmaceutical companies paying physicians to target vulnerable populations for financial gain and the purchase of healthcare data by advertising giants such as Amazon, Google, and Microsoft.…”

A Survey on Differential Privacy for Medical Data Analysis

“…Revolving around Laplace mechanism, researchers have extended a series of works to provide better data utility and privacy guarantee. Li et al [ 55 ] proposed an improved randomized method to tackle stream medical data collection with a single attribute. That method incorporates random response and Laplace mechanism, further improving the availability of mean value estimation with stream data in medical wearable devices.…”