A Location Trajectory Privacy Protection Method Based on Generative Adversarial Network and Attention Mechanism
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Abstract
User location trajectory refers to the sequence of geographic location information that records the user’s movement or stay within a period of time and is usually used in mobile crowd sensing networks, in which the user par... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Medical Research and Treatments · Educational Reforms and Innovations · Regional Development and Environment
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References (33)
Show source-provided references
1. PrivyTo: A privacy-preserving location-sharing platform10.1111/tgis.12924 ↗
2. Privacy protection for Wi-Fi location positioning systems
3. Deep-distributed-learning-based POI recommendation under mobile-edge networks10.1109/JIOT.2022.3202628 ↗
4. Context-aware semantic annotation of mobility records
5. The livehoods project: Utilizing social media to understand the dynamics of a city
6. Human mobility prediction with region-based flows and water consumption10.1109/ACCESS.2021.3090582 ↗
7. On the privacy protection of indoor location dataset using anonymization10.1016/j.cose.2022.102665 ↗
8. Privacy-friendly statistical counting for pedestrian dynamics10.1016/j.comcom.2023.09.009 ↗
9. Spatial, temporal, and content analysis of Twitter for wildfire hazards10.1007/s11069-016-2329-6 ↗
10. Geographic situational awareness: Mining tweets for disaster preparedness, emergency response, impact, and recovery10.3390/ijgi4031549 ↗
11. A privacy-preserving trajectory data synthesis framework based on differential privacy
12. Modelling perceived risks to personal privacy from location disclosure on online social networks10.1080/13658816.2019.1654109 ↗
13. Dobson · Urban Informatics · 2021 · 56710.1007/978-981-15-8983-6_32 ↗
14. Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users
15. CATS: Conditional adversarial trajectory synthesis for privacy-preserving trajectory data publication using deep learning approaches10.1080/13658816.2023.2262550 ↗
16. Privacy versus location accuracy in opportunistic wearable networks
17. Privacy protection-based incentive mechanism for mobile crowdsensing10.1016/j.comcom.2020.03.027 ↗
18. An exploratory assessment of the effectiveness of geomasking methods on privacy protection and analytical accuracy for individual-level geospatial data10.1080/15230406.2022.2056510 ↗
19. Research on user privacy protection algorithm in location service
20. Real-location reporting based differential privacy trajectory protection for mobile crowdsensing
21. Private trajectory data publication for trajectory classification
22. Joint obfuscation of location and its semantic information for privacy protection10.1016/j.cose.2021.102310 ↗
23. MARC: A robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings10.1080/13658816.2019.1707835 ↗
24. Generative adversarial nets
25. The need for privacy with public digital contact tracing during the COVID-19 pandemic10.1016/S2589-7500(20)30133-3 ↗
26. A first look at privacy analysis of COVID-19 contact-tracing mobile applications10.1109/JIOT.2020.3024180 ↗
27. C. Troncoso et al., “Decentralized privacy-preserving proximity tracing,” 2020. doi: 10.48550/arXiv.2005.12273.
28. Privacy-preserving location-based advertising via longitudinal geo-indistinguishability10.1109/TMC.2023.3348136 ↗
29. Maximizing the utility in location-based mobile advertising10.1109/TKDE.2020.2986198 ↗
30. Privacy enhanced matrix factorization for recommendation with local differential privacy10.1109/TKDE.2018.2805356 ↗
31. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs10.1109/TSMC.2014.2327053 ↗
32. LSTM-TrajGAN: A deep learning approach to trajectory privacy protection
33. Privacy vs utility analysis when applying differential privacy on machine learning classifiers