Results 271 to 280 of about 369,264 (297)

Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model

2019 20th IEEE International Conference on Mobile Data Management (MDM), 2019
With the proliferation of location-aware devices, trajectory data have been widely collected, published, and analyzed in real-life applications. However, published trajectory data often contain sensitive attributes, so an attacker who can identify an individual from such data through record linkage, attribute linkage, or similarity attacks can gain ...
Lin Yao, Xinyu Wang, Xin Wang, Haibo Hu, Guowei Wu   +4 more
openaire   +1 more source

Personalized trajectory privacy data publishing scheme based on differential privacy

Internet of Things
Peiqian Liu, Duoduo Wu, Zihao Shen, Hui Wang, Kun Liu   +4 more
openaire   +3 more sources

Privacy-Preserving Publishing of Pseudonym-Based Trajectory Location Data Set

2013 International Conference on Availability, Reliability and Security, 2013
Anonymization is a common technique for publishing a location data set in a privacy-preserving way. However, such an anonymized data set lacks trajectory information of users, which could be beneficial to many location-based analytic services. In this paper, we present a dynamic pseudonym scheme for constructing alternate possible paths of mobile users
Ken Mano, Kazuhiro Minami, Hiroshi Maruyama   +2 more
openaire   +1 more source

Privacy Preservation for Trajectory Data Publishing and Heuristic Approach

2017
With the increasing of location-aware devices, it is easy to collect the trajectory of a person which can be represented as a sequence of visited locations with regard to timestamps. The trajectory data publishing can be useful in real-life applications, such as location-based advertising, traffic management, and geo-marketing.
Nattapon Harnsamut, Juggapong Natwichai
openaire   +1 more source

Adding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization

2020 IEEE International Conference on Big Data and Smart Computing (BigComp), 2020
Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they tend to protect by reducing data depends on a feature ...
Rashid Tojiboev, Wookey Lee, Charles Cheolgi Lee   +2 more
openaire   +1 more source

Segment Clustering Based Privacy Preserving Algorithm for Trajectory Data Publishing

2018
In recent years, privacy preserving data publishing has become a hot research point in the area of privacy preserving. The (k, δ)-anonymity model exploited the inherent uncertainty of the trajectory acquisition system, but could not consider the case that the uncertain threshold of trajectory was variable in practical applications. Therefore, the (k, δ)
Li Fengyun, Xue Junchao, Sun Dawei, Gao Yanfang   +3 more
openaire   +1 more source

Trajectory Data Publishing Method with Local Differential Privacy

2023 IEEE International Conference on Unmanned Systems (ICUS), 2023
Haidong Wu, Xingfa Shen, Chuntong Geng
openaire   +1 more source

Vehicle Trajectory Data Publishing Mechanism Based on Differential Privacy

2021 China Automation Congress (CAC), 2021
Shanbin Li, Zhiyin Qi, Qinxue Li
openaire   +1 more source

Privacy Preserved Data Publishing of Moving Object Trajectories

2023
openaire   +1 more source

Optimization of Privacy Budget Allocation In Differential Privacy-Based Public Transit Trajectory Data Publishing for Smart Mobility Applications

IEEE Transactions on Intelligent Transportation Systems, 2023
Chenxi Chen, Xianbiao Hu, Qing Tang
exaly