A5001570342- Privacy-Preserving Technologies in Data
- Human Mobility and Location-Based Analysis
- Vehicular Ad Hoc Networks (VANETs)
- Mobile Crowdsensing and Crowdsourcing
- Privacy, Security, and Data Protection
- Traffic Prediction and Management Techniques
- Spam and Phishing Detection
- Internet Traffic Analysis and Secure E-voting
- IoT and Edge/Fog Computing
- Sharing Economy and Platforms
- Blockchain Technology Applications and Security
- Brain Tumor Detection and Classification
- Transportation and Mobility Innovations
- Advanced Data and IoT Technologies
Xiangtan University
2020-2025
The number of vehicles has increased year by year, especially individual vehicles. In addition to meeting basic transportation needs, are expected serve varied location-based services and applications for humans. However, it can constitute severe risks privacy. this article, we concentrate on one the most sensitive information, namely, social relationships, that be inferred from vehicle mobility data. We propose a relationship inference model, which provides new perspective privacy...
Although crowdsensing has emerged as a popular information collection paradigm, its security and privacy vulnerabilities have come to the forefront in recent years. However, one big limitation of previous research is that domain are typically considered separately. Therefore, it unclear whether defense methods will unexpected impact on domain. To bridge this gap, paper, we propose novel Disguise-based Data Poisoning Attack (DDPA) against differentially private systems empowered with truth...
Although users can obtain various services by sharing their location information online with location-based service providers, it reveals sensitive about users. However, existing privacy-preserving techniques in the scenario suffer from following shortcomings. First, they model correlations between real trajectory and distorted as undirected, which makes them unable to accurately quantify data privacy leakage caused trajectory. Second, are protect semantic privacy, i.e., attackers victims'...
In this paper, we explore data poisoning attacks and their defenses in local differential privacy (LDP)-based crowdsensing systems. First, construct launched by corrupted workers to subvert results tampering information reported. Specifically, the are formulated as a bi-level optimization problem where attackers strive conceal malicious behavior delicately exploiting noise perturbation introduced LDP protocols. way, can not be detected, even with weight-based truth discovery methods. Due...
For academic research and business intelligence, trajectory data has been widely collected analyzed. Releasing to a third party may lead serious privacy leakage, which spawned considerable researches on protection technology. However, existing work suffers from several shortcomings. They either focus point-based location privacy, ignoring the spatio-temporal correlations among locations within trajectory, or they protect of each user separately without considering leakage social relationship...
The ubiquity of private vehicles with positioning services leaves a great deal mobility data in the physical world, which supports abundant mobile applications Internet Vehicles. Despite numerous desirable features that provide, social relationship privacy inherent vehicle has gone little notice. relevant work concentrates on relation either only considers temporal and spatial features, attempts to obtain explicit venue cooccurrence frequency statistics data, or characterizes semantics...
With the widespread use of Ride-on Demand (RoD) system, many privacy issues have been exposed, and there is growing concern about whether private information will be leaked. For this problem, our previous work addressed issue user's initial final location leakage provided a strong utility guarantee in RoD system. Further, trajectory also important it could contain lot user, such as health or identity, so it's to publish distorted productive trajectory. purpose, paper, we provide protection...