Article
Computer Science, Hardware & Architecture
Minglai Shao, Jianxin Li, Qiben Yan, Feng Chen, Hongyi Huang, Xunxun Chen
Summary: Mobile devices have become an important part of daily life, with increasing concerns about potential privacy leakage, especially location privacy. This article introduces a novel location inference attack framework, iTracker, which can recover multiple trajectories simultaneously using a structured sparsity model, outperforming existing recovery algorithms.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Information Systems
Huiwen Luo, Haoming Zhang, Shigong Long, Yi Lin
Summary: The study introduces a novel location perturbation method based on geo-indistinguishability to protect privacy, focusing on preserving frequently occurring position points and proposing a privacy metric approach derived from information entropy. Experimental results confirm the superiority of the proposed strategy in terms of quality loss and privacy metric.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Civil
Chuan Xu, Yingyi Ding, Chao Chen, Yong Ding, Wei Zhou, Sheng Wen
Summary: This paper proposes a personalized location privacy protection scheme based on differential privacy to protect the privacy of location-based services in vehicular networks. By establishing a utility model and a sensitivity distance index, differentiated protection for users' different locations is achieved. An optimal solution for false location is obtained through a multi-objective optimization model.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Zuobin Xiong, Zhipeng Cai, Qilong Han, Arwa Alrawais, Wei Li
Summary: The development of computer vision and deep neural networks has greatly advanced visual perception, especially in the field of autonomous vehicles. This article focuses on protecting individuals' location privacy by utilizing generative adversarial networks to generate privacy-preserving outputs from original camera data, while maintaining a balance between privacy and data utility. Results from real-data experiments demonstrate the superiority of the proposed models in preserving utility and protecting privacy for autonomous vehicles' images and videos.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Chenxi Qiu, Anna Cinzia Squicciarini, Ce Pang, Ning Wang, Ben Wu
Summary: In this paper, the authors address the issue of Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. They propose a location obfuscation strategy to minimize quality loss while satisfying geo-indistinguishability. They approximate VLP to a linear programming problem and employ discretization and constraint reduction techniques to improve time efficiency. Experimental results demonstrate the superiority of their approach in terms of both quality-of-service and privacy.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Engineering, Civil
Yuchu He, Zhijuan Jia, Mingsheng Hu, Geng Zhang, Hanjie Dong
Summary: Through intelligent analysis and prediction of vehicle trip data, this technology enhances user driving experience and improves urban traffic conditions. However, existing models suffer from insufficient information, data deviation, and linear feature addition. To address these drawbacks, a novel method named HAHIF is proposed, which achieves good prediction results on a public dataset.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Shuai Xu, Dechang Pi, Jiuxin Cao, Xiaoming Fu
Summary: The study proposes a two-stage framework consisting of a temporal base model and a location prediction model to predict user consumption locations in the future. The first stage captures user latent preference using user sentimental textual reviews and hierarchical attention mechanism, while the second stage derives consumption probability towards different locations by incorporating multifaceted context information.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Theory & Methods
Zhirun Zheng, Zhetao Li, Hongbo Jiang, Leo Yu Zhang, Dengbiao Tu
Summary: In this paper, a novel semantic-aware privacy-preserving online location trajectory sharing mechanism is proposed to protect both data privacy and semantic privacy while preserving data utility. Theoretical analysis proves the effectiveness of the mechanism, and experimental evaluations show its superiority over existing mechanisms.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Information Systems
Jingyu Feng, Yin Wang, Jialin Wang, Fang Ren
Summary: This article proposes a trusted cloaking area construction scheme based on trust mechanism to protect the location privacy of vehicles. Trust value is used to identify dishonest vehicles and determine location-based service requests. Edge computing is utilized to assist trust value evaluation, while a blockchain-based method is used for rapid trust value assessment.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Xiufang Shi, Dan Yu, Minglei Fu, Wen-An Zhang
Summary: This article investigates the tradeoff between location privacy and localization accuracy in cooperative localization. It proposes an incentive mechanism based on contract theory to protect privacy and ensure accuracy, and verifies its performance and feasibility through extensive simulations.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Theory & Methods
Yao Xiao, Amelie Chi Zhou, Xuan Yang, Bingsheng He
Summary: This paper proposes a privacy-preserving workflow scheduling algorithm named PPPS, aiming to minimize inter-DC data transfer time while satisfying data privacy requirements in a geo-distributed environment. Experimental results show that PPPS significantly reduces workflow execution time compared to other algorithms and can satisfy complex data privacy constraints.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Dan Yu, Xiufang Shi, Li Chai, Wen-An Zhang, Jiming Chen
Summary: This work investigates the tradeoff between target localization accuracy and cooperative nodes' (CNs) location privacy in a cooperative localization scenario with mobile nodes. The temporal correlations among target states and CN locations are considered, and a game-based incentive mechanism is proposed to balance the objectives of the CNs and the target. Extensive numerical results validate the effectiveness of the proposed incentive mechanism in terms of localization accuracy and CNs' utilities.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Review
Computer Science, Artificial Intelligence
Inda Kreso, Amra Kapo, Lejla Turulja
Summary: With the increasing amount of data and information in the modern era, data mining has become a crucial method to search for useful data patterns and predictions. However, privacy-preserving data mining (PPDM) techniques aim to protect privacy while providing accurate data mining results.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Engineering, Civil
Elena Daraio, Luca Cagliero, Silvia Chiusano, Paolo Garza
Summary: This paper discusses the use of Location-Based Social Networks (LBSN) to characterize citizens' activities in urban areas and proposes to complement LBSN data with mobility data in the analysis. The results show that the two data sources are complementary in specific Points-Of-Interest (POI) categories and interchangeable in many spatio-temporal conditions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Ben Niu, Yahong Chen, Zhibo Wang, Fenghua li, Boyang Wang, Hui Li
Summary: This paper presents mechanisms based on geo-indistinguishability for preserving location privacy, and identifies their vulnerabilities under long-term observation attacks. In response to these vulnerabilities, a novel mechanism called Eclipse is proposed to bridge the gap between location protection and service usability. By obfuscating locations and hiding each location based on an anonymity set, the Eclipse mechanism effectively suppresses information leakage under long-term observation attacks.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)