Skip to main content

Dynamic-Parameter Genetic Algorithm for Multi-objective Privacy-Preserving Trajectory Data Publishing

  • Conference paper
  • First Online:
Web Information Systems Engineering – WISE 2024 (WISE 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15440))

Included in the following conference series:

  • 237 Accesses

Abstract

Nowadays, trajectory data is widely accessible and can be beneficial for various practical applications, such as location-based services, personalized recommendation, and traffic management. Despite the immense benefits in these scenarios, trajectories can reveal highly sensitive information about individuals, such as personal characteristics, movement patterns, visited locations, and social connections. Consequently, it is imperative to prioritize protecting privacy when conducting trajectory analyses. Existing privacy-preserving techniques focus on optimizing data utility but often overlook the diverse requirements for privacy preservation. To address this limitation, this paper aims to maximize both privacy and utility as a multi-objective optimization problem for Privacy-Preserving Trajectory Data Publishing (PPTDP). We propose a novel algorithm called Dynamic-Parameter Genetic Algorithm (DPGA) that utilizes the non-dominated sorting multi-objective optimization approach and genetic algorithm (GA). This algorithm designs the mutation and crossover strategies to dynamically adjust the mutation and crossover parameters and improve the solution’s quality. It also adopts a scramble mutation strategy that helps to achieve better population diversity. Extensive experiments demonstrate the efficiency of the proposed algorithm in terms of solution accuracy and convergence result.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abul, O., Bonchi, F., Nanni, M.: Never walk alone: uncertainty for anonymity in moving objects databases. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 376–385. IEEE (2008)

    Google Scholar 

  2. Abul, O., Bonchi, F., Nanni, M.: Anonymization of moving objects databases by clustering and perturbation. Inf. Syst. 35(8), 884–910 (2010)

    Article  Google Scholar 

  3. Chen, H., Wu, G., Pedrycz, W., Suganthan, P.N., Xing, L., Zhu, X.: An adaptive resource allocation strategy for objective space partition-based multiobjective optimization. IEEE Trans. Syst. Man Cybern. Syst. 51(3), 1507–1522 (2019)

    Google Scholar 

  4. Damia, A., Esnaashari, M., Parvizimosaed, M.: Adaptive genetic algorithm based on mutation and crossover and selection probabilities. In: 2021 7th International Conference on Web Research (ICWR), pp. 86–90. IEEE (2021)

    Google Scholar 

  5. Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)

    Article  Google Scholar 

  6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  7. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1

    Chapter  Google Scholar 

  8. Ge, Y.F., Bertino, E., Wang, H., Cao, J., Zhang, Y.: Distributed cooperative coevolution of data publishing privacy and transparency. ACM Trans. Knowl. Discov. Data 18(1), 1–23 (2023)

    Article  Google Scholar 

  9. Ge, Y.F., Orlowska, M., Cao, J., Wang, H., Zhang, Y.: Knowledge transfer-based distributed differential evolution for dynamic database fragmentation. Knowl.-Based Syst. 229, 107325 (2021). https://doi.org/10.1016/j.knosys.2021.107325

    Article  Google Scholar 

  10. Ge, Y.-F., Orlowska, M., Cao, J., Wang, H., Zhang, Y.: MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation. VLDB J. 1–19 (2021). https://doi.org/10.1007/s00778-021-00718-w

  11. Ge, Y.F., et al.: Evolutionary dynamic database partitioning optimization for privacy and utility. IEEE Trans. Dependable Secure Comput. (2023)

    Google Scholar 

  12. Ge, Y.F., Wang, H., Cao, J., Zhang, Y.: An information-driven genetic algorithm for privacy-preserving data publishing. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds.) WISE 2022. LNCS, vol. 13724, pp. 340–354. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20891-1_24

    Chapter  Google Scholar 

  13. Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., Prasath, V.S.: Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information 10(12), 390 (2019)

    Article  Google Scholar 

  14. Jahan, S., Ge, Y.F., Kabir, E., Wang, H.: Analysis and protection of public medical dataset: from privacy perspective. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds.) HIS 2023. LNCS, vol. 14305, pp. 79–90. Springer, Cham (2023). https://doi.org/10.1007/978-981-99-7108-4_7

    Chapter  Google Scholar 

  15. Jin, F., Hua, W., Francia, M., Chao, P., Orlowska, M.E., Zhou, X.: A survey and experimental study on privacy-preserving trajectory data publishing. IEEE Trans. Knowl. Data Eng. 35(6), 5577–5596 (2022)

    Google Scholar 

  16. Jin, F., Hua, W., Ruan, B., Zhou, X.: Frequency-based randomization for guaranteeing differential privacy in spatial trajectories. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 1727–1739. IEEE (2022)

    Google Scholar 

  17. Kabir, M.E., Mahmood, A.N., Wang, H., Mustafa, A.K.: Microaggregation sorting framework for k-anonymity statistical disclosure control in cloud computing. IEEE Trans. Cloud Comput. 8(2), 408–417 (2020). https://doi.org/10.1109/tcc.2015.2469649

    Article  Google Scholar 

  18. Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 106–115. IEEE (2006)

    Google Scholar 

  19. Lv, Z., Lou, R., Singh, A.K.: Ai empowered communication systems for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 22(7), 4579–4587 (2020)

    Article  Google Scholar 

  20. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 3-es (2007)

    Google Scholar 

  21. Sweeney, L.: k-anonymity: a model for protecting privacy. Internat. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  22. Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing uncertainty in moving objects databases. ACM Trans. Database Syst. (TODS) 29(3), 463–507 (2004)

    Article  Google Scholar 

  23. Tu, Z., Zhao, K., Xu, F., Li, Y., Su, L., Jin, D.: Protecting trajectory from semantic attack considering \(k\)-anonymity, \(l\)-diversity, and \(t\)-closeness. IEEE Trans. Netw. Serv. Manage. 16(1), 264–278 (2019). https://doi.org/10.1109/tnsm.2018.2877790

    Article  Google Scholar 

  24. Wang, H., Jiang, X., Kambourakis, G.: Special issue on security, privacy and trust in network-based big data. Inf. Sci. 318, 48–50 (2015)

    Article  MathSciNet  Google Scholar 

  25. Wang, H., Zhang, Y., Cao, J.: Ubiquitous computing environments and its usage access control. In: Proceedings of the 1st International Conference on Scalable Information Systems, pp. 6–es (2006)

    Google Scholar 

  26. Yin, J., Chen, G., Hong, W., Cao, J., Wang, H., Miao, Y.: A heterogeneous graph-based semi-supervised learning framework for access control decision-making. World Wide Web 27(4), 35 (2024)

    Article  Google Scholar 

  27. Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web 1–23 (2022)

    Google Scholar 

  28. Yin, J., Tang, M., Cao, J., You, M., Wang, H., Alazab, M.: Knowledge-driven cybersecurity intelligence: software vulnerability coexploitation behavior discovery. IEEE Trans. Industr. Inf. 19(4), 5593–5601 (2022)

    Article  Google Scholar 

  29. You, M., Ge, Y.F., Wang, K., Wang, H., Cao, J., Kambourakis, G.: Hierarchical adaptive evolution framework for privacy-preserving data publishing. World Wide Web 27(4), 49 (2024)

    Article  Google Scholar 

  30. You, M., et al.: A knowledge graph empowered online learning framework for access control decision-making. World Wide Web 26(2), 827–848 (2023)

    Article  Google Scholar 

  31. Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 316–324 (2011)

    Google Scholar 

  32. Yuan, J., et al.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108 (2010)

    Google Scholar 

  33. Zhang, J., Huang, Q., Huang, Y., Ding, Q., Tsai, P.W.: DP-TrajGAN: a privacy-aware trajectory generation model with differential privacy. Futur. Gener. Comput. Syst. 142, 25–40 (2023)

    Article  Google Scholar 

  34. Zhang, X., Tian, Y., Jin, Y.: A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(6), 761–776 (2014)

    Article  Google Scholar 

  35. Zhao, B., Chen, W.N., Wei, F.F., Liu, X., Pei, Q., Zhang, J.: PEGA: a privacy-preserving genetic algorithm for combinatorial optimization. IEEE Trans. Cybern. (2024)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong-Feng Ge .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jahan, S., Ge, YF., Wang, H., Kabir, E. (2025). Dynamic-Parameter Genetic Algorithm for Multi-objective Privacy-Preserving Trajectory Data Publishing. In: Barhamgi, M., Wang, H., Wang, X. (eds) Web Information Systems Engineering – WISE 2024. WISE 2024. Lecture Notes in Computer Science, vol 15440. Springer, Singapore. https://doi.org/10.1007/978-981-96-0576-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-96-0576-7_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-0575-0

  • Online ISBN: 978-981-96-0576-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics