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Time-series Location Data Collection and Analysis Under Local Differential Privacy
Kijung Jung, Hyukki Lee, Yon Dohn Chung
http://doi.org/10.5626/JOK.2022.49.4.305
As the prevalence of smart devices that can generate location data, the number of location-based services is exploding. Since the user’s location data are sensitive information, if the original data are utilized in their original form, the privacy of individuals could be breached. In this study, we proposed a time-series location data collection and analysis method that satisfies local differential privacy, which is a strong privacy model for the data collection environment and considers the characteristics of time-series location data. In the data collection process, the location of an individual is expressed as a bit array. After that, each bit of the array is perturbed by randomized responses for privacy preservation. In the data analysis process, we analyzed the location frequency using hidden Markov model. Moreover, we performed additional spatiotemporal correlation analysis, which is not possible in the existing analysis methods. To demonstrate the performance of the proposed method, we generated trajectory data based on the Seoul subway and analyzed the results of our method.
Health State Clustering and Prediction Based on Bayesian HMM
http://doi.org/10.5626/JOK.2017.44.10.1026
In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.
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