School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, People's Republic of China.
PLoS One. 2022 Aug 17;17(8):e0272766. doi: 10.1371/journal.pone.0272766. eCollection 2022.
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.
可穿戴设备采集的个人数据包含丰富的隐私信息。在不影响可穿戴设备服务数据采集的情况下，实现用户数据的个人隐私保护至关重要。针对采集的单属性数值流数据，提出了一种基于局部差分隐私的采集方案。首先，对可穿戴设备采集的流数据点进行屏蔽，以识别显著点，并根据分配的隐私预算使用自适应拉普拉斯机制为这些显著点添加噪声；然后，收集器对添加噪声的显著点进行重建和拟合，从而保护数据的个人隐私。该方案在心率数据集上进行了实验，结果表明，当隐私预算为 0.5（即更高的隐私保护强度）时，平均相对误差为 0.12，比 Kim 等人的方案低 57.78%。在满足用户隐私保护的前提下，提高了可穿戴设备流数据均值估计的可用性。
