Research on Location Privacy Protection Technology in Wireless Sensor Networks Based on Big Data
DOI:
https://doi.org/10.13052/jcsm2245-1439.1262Keywords:
Digital footprint, False position technology, Generate confrontation network, Synthetic track, TUL taskAbstract
The digital footprint in wireless sensor networks can bring great academic and commercial value, but it will also bring the risk of privacy disclosure to users. This study discusses the location privacy protection methods in location based service under multiple scenarios. In the experiment, a false location filtering algorithm for real-time location request scenarios and a false path generation model for offline location release scenarios are proposed. The false position filtering algorithm is implemented based on the idea of a large top heap selection query. The algorithm can construct false position candidate sets and filter false positions. The false track generation model combines the false position technology and the generative adversarial networks model, which mainly protects the user’s track data by synthesizing tracks. In the attacker’s recognition experiment of a real location. The minimum distance between the false locations generated by the algorithm proposed in the study is above 400 m and the generation time does not exceed 5 ms, generating a better set of false locations in terms of both effectiveness and efficiency. Compared with several commonly used privacy-preserving algorithms, the proposed algorithm has the lowest probability of being identified with real locations, with no more than 21% overall, and is almost independent of the k value. the recognition accuracy of the trajectory user link task decreases from over 90% to about 34%, indicating that the proposed fake trajectory generation model can effectively protect users’ data privacy. The experimental results demonstrate that the algorithm and model proposed in the study can quickly generate physically dispersed and semantically diverse sets of fake locations and effectively protect users’ trajectory privacy, which is important for users’ digital footprint privacy protection.
Downloads
References
Mou J. Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area. IEEE Access, 2022, 10: 16644–16654.
Tucakovi’c L, Boji’c L. Computer-based personality judgments from digital footprints: theoretical considerations and practical implications in politics. Srpska politièka misao, 2022, 74(4): 235–253.
Quach S, Thaichon P, Martin K D, Weaven S, Palmatier R W. Digital technologies: Tensions in privacy and data. Journal of the Academy of Marketing Science, 2022, 50(6): 1299–1323.
Kolasa K, Ken Redekop W, Berler A, Zah V, Asche C V. Future of data analytics in the era of the general data protection regulation in Europe. PharmacoEconomics, 2020, 38(10): 1021–1029.
Andrew J, Baker M. The general data protection regulation in the age of surveillance capitalism. Journal of Business Ethics, 2021, 168: 565–578.
Wei K, Li J, Ding M, Ma C, Yang H H, Farokhi F, Jin S, Quek T Q S, Poor H V. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security, 2020, 15: 3454–3469.
Yadav V K, Verma S, Venkatesan S. Linkable privacy-preserving scheme for location-based services. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 7998–8012.
Farouk F, Alkady Y, Rizk R. Efficient privacy-preserving scheme for location based services in VANET system. IEEE Access, 2020, 8: 60101–60116.
Wu Z, Wang R, Li Q, Lian X, Xu G, Chen E, Liu X. A location privacy-preserving system based on query range cover-up or location-based services. IEEE Transactions on Vehicular Technology, 2020, 69(5): 5244–5254.
Hassan M U, Rehmani M H, Chen J. Differential privacy techniques for cyber physical systems: a survey. IEEE Communications Surveys & Tutorials, 2019, 22(1): 746–789.
Wu Z, Li G, Shen S, Lian X, Chen E, Xu G. Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web, 2021, 24: 25–49.
Zhang S, Li X, Tan Z, Peng T, Wang G. A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services. Future Generation Computer Systems, 2019, 94: 40–50.
Liu J, Wang S. All-dummy k-anonymous privacy protection algorithm based on location offset. Computing, 2022, 104(8): 1739–1751.
Zhang S, Mao X, Choo K K R, Peng T, Wang G. A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services. Information Sciences, 2020, 527: 406–419.
Qu Y, Yu S, Zhou W, Tian Y. Gan-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Transactions on Network Science and Engineering, 2020, 7(4): 2576–2586.
Xiong Z, Cai Z, Han Q, Alrawais A, Li W. ADGAN: Protect your location privacy in camera data of auto-driving vehicles. IEEE Transactions on Industrial Informatics, 2020, 17(9): 6200–6210.
Huang C, Chen S, Zhang Y, Zhou W, Rodrigues J. A robust approach for privacy data protection: IoT security assurance using generative adversarial imitation learning. IEEE Internet of Things Journal, 2021, 9(18): 17089–17097.
Chen S, Fu A, Yu S, Ke H, Su M. DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication. Soft Computing, 2021, 25: 7325–7339.
Masnabadi N, Hosseinali F, Bahramian Z. Developing a spatial and temporal density-based clustering algorithm to extract stop locations from the user’s trajectory. Engineering Journal of Geospatial Information Technology, 2021, 9(2): 105–128.
Sun G, Chang V, Ramachandran M, Sun Z, Li J, Yu H, Liao D. Efficient location privacy algorithm for Internet of Things (IoT) services and applications. Journal of Network and Computer Applications, 2017, 89: 3–13.
Chung B, Ptasznik A, Wu D, Bonaci T. Privacy and location-based services. IEEE Potentials, 2022, 41(4): 31–37.
Wu L, Wei X, Meng L, Zhao S, Wang H. Privacy-preserving location-based traffic density monitoring. Connection Science, 2022, 34(1): 874–894.
Gao S, Rao J, Liu X, Kang Y, Haung Q, App J. Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users. Journal of Spatial Information Science, 2019, 19: 105–129.
Wang T, Zeng J, Bhuiyan M Z A, Tian H, Cai Y, Chen Y, Zhong B. Trajectory privacy preservation based on a fog structure for cloud location services. IEEE Access, 2017, 5: 7692–7701.
Chen X, Xu J, Zhou R, Chen W, Fang J, Liu C. TrajVAE: A Variational AutoEncoder model for trajectory generation. Neurocomputing, 2021, 428: 332–339.
Parmar D, Rao U P. Towards privacy-preserving dummy generation in location-based services. Procedia Computer Science, 2020, 171: 1323–1326.
Jiang J, Han G, Wang H, Guizani M. A survey on location privacy protection in wireless sensor networks. Journal of Network and Computer Applications, 2019, 125: 93–114.
Huang Q, Du J, Yan G, Yang Y, Wei Q. Privacy-preserving spatio-temporal keyword search for outsourced location-based services. IEEE Transactions on Services Computing, 2021, 15(6): 3443–3456.
Manju A B, Subramanian S. Fog-Assisted Privacy Preservation Scheme for Location-Based Services Based on Trust Relationship. International Journal of Grid and High Performance Computing (IJGHPC), 2020, 12(4): 48–62.
Yang P, Xiong N, Ren J. Data security and privacy protection for cloud storage: A survey. IEEE Access, 2020, 8: 131723–131740.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Cyber Security and Mobility
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
