Trajectory Data Protection based on Differential Privacy k-means

2020 ◽  
Author(s):  
Qiyuan Xu ◽  
Zhenping Chen ◽  
Baochuan Fu ◽  
Xuelian Shao
Keyword(s):  
Data Protection ◽  
Differential Privacy ◽  
Trajectory Data

scholarly journals Differential Privacy Trajectory Data Protection Algorithm Based on Polar Coordinate Transformation

2020 ◽  
Author(s):  
Zhenzhen Zhang ◽  
Jianping Cai ◽  
Lan Sun ◽  
Yongyi Guo ◽  
Yubing Qiu ◽  
...  
Keyword(s):  
Coordinate System ◽  
Data Protection ◽  
Differential Privacy ◽  
Polar Coordinates ◽  
Trajectory Data ◽  
Rectangular Coordinate System ◽  
Data Release ◽  
Information Research ◽  
Polar Coordinate ◽  
System Representation

Differential privacy technology has been widely used in the issue of trajectory data release. Improving the availability of data release under the premise of ensuring privacy and security is one of its basic research goals. At present, most trajectory data release methods use a rectangular coordinate system to represent location information. Research has shown that the availability of published data cannot be optimized through the rectangular coordinate system. In order to improve the effect of trajectory data release, this paper proposes a differential privacy trajectory data protection algorithm based on polar coordinates. First, the stay point detection method is used to find frequent stay points in the trajectory and the key location points related to personal privacy are detected by the type of location points. Then, this paper converts the rectangular coordinate system representation of the key position points to the polar coordinate system representation, and implement differential privacy trajectory data release by adding noise to the key position points represented by the polar coordinates. Experiments show that the algorithm proposed in this paper effectively improves the usability of trajectory data on real data sets.

OPTDP: Towards Optimal Personalized Trajectory Differential Privacy for Trajectory Data Publishing

2021 ◽  
Author(s):  
Wenqing Cheng ◽  
Ruxue Wen ◽  
Haojun Huang ◽  
Wang Miao ◽  
Chen Wang
Keyword(s):  
Differential Privacy ◽  
Data Publishing ◽  
Trajectory Data

Trajectory Data Protection

2021 ◽  
pp. 47-68
Author(s):  
Chao Chen ◽  
Daqing Zhang ◽  
Yasha Wang ◽  
Hongyu Huang
Keyword(s):  
Data Protection ◽  
Trajectory Data

scholarly journals DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167754-167765
Author(s):  
SongYan Li ◽  
Zhaobin Liu ◽  
Zhiyi Huang ◽  
Haoze Lyu ◽  
Zhiyang Li ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Protection ◽  
Differential Privacy ◽  
Wireless Sensor ◽  
Network Data

Achieving differential privacy of trajectory data publishing in participatory sensing

2017 ◽  
Vol 400-401 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Li ◽  
Liehuang Zhu ◽  
Zijian Zhang ◽  
Rixin Xu
Keyword(s):  
Differential Privacy ◽  
Data Publishing ◽  
Participatory Sensing ◽  
Trajectory Data

scholarly journals An Analysis of Differential Privacy Research in Location and Trajectory Data

2020 ◽  
Author(s):  
Fatima Zahra Errounda ◽  
Yan Liu
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
State Of The Art ◽  
Original Data ◽  
Privacy Preserving ◽  
The State ◽  
Trajectory Data ◽  
Powerful Technique ◽  
Location Data ◽  
Single User

Abstract Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

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