scholarly journals Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 1993 ◽  
Author(s):  
Qingqing Xie ◽  
Liangmin Wang
Keyword(s):  
Privacy Preserving ◽  
Mobile Sensing ◽  
Location Based Service ◽  
Sensing Data

Enabling reputation and trust in privacy-preserving mobile sensing

2014 ◽  
Vol 13 (12) ◽  
pp. 2777-2790 ◽  
Author(s):  
Xinlei Wang ◽  
Wei Cheng ◽  
Prasant Mohapatra ◽  
Tarek Abdelzaher
Keyword(s):  
Privacy Preserving ◽  
Mobile Sensing

Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients (Preprint)

2021 ◽  
Author(s):  
Joanne Zhou ◽  
Bishal Lamichhane ◽  
Dror Ben-Zeev ◽  
Andrew Campbell ◽  
Akane Sano
Keyword(s):  
Prediction Models ◽  
Gaussian Mixture ◽  
Mobile Sensing ◽  
Cluster Models ◽  
Sensor Data ◽  
Sensing Data ◽  
Model Based ◽  
Clustering Model ◽  
Relapse Prediction ◽  
Lower Variability

BACKGROUND Behavioral representations obtained from mobile sensing data could be helpful for the prediction of an oncoming psychotic relapse in schizophrenia patients and delivery of timely interventions to mitigate such relapse. OBJECTIVE In this work, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. METHODS We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data and thus provide differing behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. RESULTS The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). While GMM based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread likely indicating heterogeneous behavioral characterizations. PAM model based clusters on the other hand had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042. CONCLUSIONS Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine as well as atypical behavioral trends. In this work, we used GMM and PAM-based cluster models to obtain behavioral trends in schizophrenia patients. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful to enable timely interventions.

Privacy-preserving location-based service protocols with flexible access

2019 ◽  
Vol 20 (3) ◽  
pp. 412
Author(s):  
Shuyang Tang ◽  
Shengli Liu ◽  
Xinyi Huang ◽  
Zhiqiang Liu
Keyword(s):  
Privacy Preserving ◽  
Location Based Service

scholarly journals Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Chen ◽  
Shengbao Zheng ◽  
Zhengqiu Weng
Keyword(s):  
Mobile Node ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Node Mobility ◽  
Mobile Nodes ◽  
Mobile Crowd Sensing ◽  
Sensitive Data ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Mobile Crowd

Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach called P3 to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper-layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.

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