Real-time and private spatio-temporal data aggregation with local differential privacy

2020 ◽  
Vol 55 ◽  
pp. 102633
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Author(s):  
Naonori Ueda ◽  
Futoshi Naya

Machine learning is a promising technology for analyzing diverse types of big data. The Internet of Things era will feature the collection of real-world information linked to time and space (location) from all sorts of sensors. In this paper, we discuss spatio-temporal multidimensional collective data analysis to create innovative services from such spatio-temporal data and describe the core technologies for the analysis. We describe core technologies about smart data collection and spatio-temporal data analysis and prediction as well as a novel approach for real-time, proactive navigation in crowded environments such as event spaces and urban areas. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. We show the effectiveness of our navigation approach by computer simulation using artificial people-flow data.


2004 ◽  
pp. 1377-1380 ◽  
Author(s):  
M MOKBEL ◽  
X XIONG ◽  
W AREF ◽  
S HAMBRUSCH ◽  
S PRABHAKAR ◽  
...  

2014 ◽  
Vol 7 (13) ◽  
pp. 1754-1759 ◽  
Author(s):  
Shiming Zhang ◽  
Yin Yang ◽  
Wei Fan ◽  
Marianne Winslett

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