Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

Yuanlong Fan; Cheng Song; Zhichao Wang

doi:10.54097/0bkcjr92

Authors

Yuanlong Fan
Cheng Song
Zhichao Wang

DOI:

https://doi.org/10.54097/0bkcjr92

Keywords:

Mechanical engineering; Manufacturing technology; Electrical automation.

Abstract

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

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References

[1] Qiu G, Tang G, Li C, et al. Differentiated Location Privacy Protection in Mobile Communication Services: A Survey from the Semantic Perception Perspective[J]. ACM Computing Surveys, 2023, 56(3): 1-36.

[2] Zhang S, Li M, Liang W, et al. A Survey of Dummy-Based Location Privacy Protection Techniques for Location-Based Services[J]. Sensors, 2022, 22(16): 6141.

[3] Jiang H, Li J, Zhao P, et al. Location privacy-preserving mechanisms in location-based services: A comprehensive survey[J]. ACM Computing Surveys (CSUR), 2021, 54(1): 1-36.

[4] Cheng S, Daochen C, Shuiping N I. k Anonymous Trajectory Privacy Protection Scheme of Personalized Differential Privacy[J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 109.

[5] Sánchez P, Bellogín A. Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective[J]. ACM Computing Surveys (CSUR), 2022, 54(11s): 1-37.

[6] Mariana C, Ricardo M, Vilela JP. A survey of privacy-preserving mechanisms for heterogeneous data types. Computer Science Review, 2021, 41: 15p.

[7] Wazirali R. A Review on Privacy Preservation of Location-Based Services in Internet of Thing. Intelligent Automation and Soft Computing, 2022, 31(2): 767-779.

[8] Li F H, Zhang C, Niu B, et al. Efficient scheme for user’s trajectory privacy[J]. Journal on Communications, 2015, 36(12): 114-123.

[9] Tian J, Zhu Q. A differential privacy trajectory data storage and publishing scheme based on radix tree[J]. Concurrency and Computation: Practice and Experience, 2023: e7731.

[10] Du X, Zhu H, Zheng Y, et al. A Semantic-Preserving Scheme to Trajectory Synthesis Using Differential Privacy[J]. IEEE Internet of Things Journal, 2023.

[11] H. Chen, S. Li and Z. Zhang, "A differential privacy based (k-Ψ)-anonymity method for trajectory data publishing," Computers, Materials & Continua, vol. 65, no.3, pp. 2665–2685, 2020.

[12] Zhao X, Pi D, Chen J. Novel trajectory privacy-preserving method based on clustering using differential privacy[J]. Expert Systems with Applications, 2020, 149: 113241.

[13] Chen S, Fu A, Shen J, et al. RNN-DP: A new differential privacy scheme base on Recurrent Neural Network for Dynamic trajectory privacy protection[J]. Journal of Network and Computer Applications, 2020, 168: 102736.

[14] Ou L, Qin Z, Liao S, et al. Releasing correlated trajectories: Towards high utility and optimal differential privacy[J]. IEEE Transactions on Dependable and Secure Computing, 2018, 17(5): 1109-1123.

[15] Shang T, Zhao Z, Ren X, et al. Differential identifiability clustering algorithms for big data analysis[J]. Science China Information Sciences, 2021, 64: 1-18.

[16] Dwork C, Roth A. The algorithmic foundations of differential privacy[J]. Foundations and Trends® in Theoretical Computer Science, 2014, 9(3–4): 211-407.

[17] Fernandes N, McIver A, Morgan C. The Laplace Mechanism has optimal utility for differential privacy over continuous queries[C]//2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). IEEE, 2021: 1-12.