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Dimension-aware under spatiotemporal constraints: an efficient privacy-preserving framework with peak density clustering

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Abstract

Location-based service (LBS) is one of the most popular applications in 5G environment. Users can enjoy plenty of intelligent services, but serious threats will be caused in LBS at the same time. In order to protect privacy while ensuring efficiency, an efficient privacy-preserving framework based on the dimension-aware under spatiotemporal constraints (DSC-EPPF) is proposed. Initially, a novel dimension-aware data preprocessing algorithm under spatiotemporal constraints (DDPA-SC) is designed, which can not only construct the dimension-aware anonymity set, but also lighten the complexity of time. Secondly, a novel candidate anonymity set constructing algorithm with ameliorated peak density clustering (CASA-PDC) is designed, which can resist the background knowledge attack by filtering out redundant anonymity set. Thirdly, the (kl)-privacy protection algorithm ((kl)-PPA) is designed for anonymity set construction. At last, three metrics, dimension-aware, CPU time as well as security with entropy are formalized. The comparison of the proposed method has also been done with other classification models viz., GIA, GITA, SCA, RS and RSABPP that revealed the superiority of the proposed method.

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Data availability statement

Data will be made available on reasonable request.

References

  1. Huang Q, Du J, Yan G, Yang Y, Wei Q (2021) Privacy-preserving spatio-temporal keyword search for outsourced location-based services. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2021.3088131

    Article  Google Scholar 

  2. Zhao P, Zhang G, Wan S et al (2020) A survey of local differential privacy for securing internet of vehicles. J Supercomput 76:8391–8412

    Article  Google Scholar 

  3. Tao LA, Psw B, Yg A, Yw A (2021) Research on the big data of traditional taxi and online car-hailing: a systematic review-sciencedirect. J Traffic Transport Eng 8(1):1–34

    Google Scholar 

  4. Li F, Yin P, Chen Y, Niu B, Li H (2020) Achieving fine-grained qos for privacy-aware users in lbss. IEEE Wirel Commun 27(3):31–37

    Article  Google Scholar 

  5. Sza B, Xm B, Kkrc C, Tao PD, Gw D (2020) A trajectory privacy-preserving scheme based on a dual-k mechanism for continuous location-based services-sciencedirect. Inf Sci 527:406–419

    Article  Google Scholar 

  6. Li S, Shen H, Sang Y, Tian H (2020) An efficient method for privacy-preserving trajectory data publishing based on data partitioning. J Supercomput 76:5276–5300

    Article  Google Scholar 

  7. Wei YC, Wu WC, Lai GH, Chu YC (2020) pISRA: privacy considered information security risk assessment model. J Supercomput 76:1468–1481

    Article  Google Scholar 

  8. Zhang L, Liu D, Chen M, Li H, Du Y (2021) A user collaboration privacy protection scheme with threshold scheme and smart contract. Inf Sci 560:183–201

    Article  Google Scholar 

  9. Mikavica B, Kosti-Ljubisavljevi A (2021) Blockchain-based solutions for security, privacy, and trust management in vehicular networks: a survey. J Supercomput 77:9520–9575

    Article  Google Scholar 

  10. Jeong YS, Kim DR, Shin SS (2021) Efficient data management techniques based on hierarchical IoT privacy using block chains in cloud environments. J Supercomput 77:9810–9826

    Article  Google Scholar 

  11. Akremi A, Rouached M (2021) A comprehensive and holistic knowledge model for cloud privacy protection. J Supercomput 77:7956–7988

    Article  Google Scholar 

  12. Liu G, Wang C, Ma X, Yang Y (2021) Keep your data locally: federated learning-based data privacy preservation in edge computing. IEEE Netw 35(2):60–66

    Article  Google Scholar 

  13. Bostanipour B, Theodorakopoulos G (2021) Joint obfuscation of location and its semantic information for privacy protection. Comput Secur 107(4):102310

    Article  Google Scholar 

  14. Sun Z, Wang Y, Cai Z, Liu T, Jiang N (2021) A two-stage privacy protection mechanism based on blockchain in mobile crowdsourcing. Int J Intell Syst 36(5):2058–2080

    Article  Google Scholar 

  15. Goncalves C, Bessa RJ, Pinson P (2021) Privacy-preserving distributed learning for renewable energy forecasting. IEEE Trans Sustain Energy 12(3):1777–1787

    Article  Google Scholar 

  16. Wei J, Lin Y, Yao X, Zhang J (2019) Differential privacy-based location protection in spatial crowdsourcing. IEEE Trans Serv Comput 15(1):45–58

    Article  Google Scholar 

  17. Wang J, Cai Z, Yu J (2020) Achieving Personalized k-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS. IEEE Trans Industr Inf 16(6):4242–4251

    Article  Google Scholar 

  18. Wang B, Guo Y, Li H, Li Z (2021) K-anonymity based location privacy protection method for location-based services in internet of thing. Concurr Comput: Practice Exp 2021:e6760

    Google Scholar 

  19. Wang T, Xu L, Zhang M, Zhang H, Zhang G (2021) A new privacy protection approach based on k-anonymity for location-based cloud services. J Circ, Syst Comput 31(5):2250083

    Article  Google Scholar 

  20. Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertainty, Fuzziness Knowl-Based Syst 10(5):557–570

    Article  MATH  Google Scholar 

  21. Gruteser M, Grunwald D (2003) Anonymous Usage of Location-based Services through spatial and temporal cloaking. In: Proc. 1st International Conference on Mobile Systems, Applications and Services. New York, USA: ACM, 2003: 3H

  22. Niu B, Zhu X, Li Q, Jie C, Hui L (2015) A novel attack to spatial cloaking schemes in location-based services. Futur Gener Comput Syst 49:125–132

    Article  Google Scholar 

  23. Andras EM, Bordenabe EN, Chatzikokolakis K, and Palamidessi C (2013) Geo-Indistinguishability: Differential Privacy for Location-Based Systems. In Proc. ACM Conference on Computer and Communications Security (CCS’13)

  24. Bordenabe EN, Chatzikokolakis K, Palamidessi C. Optimal Geo-Indistinguishable Mechanisms for Location Privacy. In Proc. CCS’14, November, Scottsdale, Arizona, USA, 2014, pp 251-262

  25. Yq A, Yj B, Msh C, Long HB, Gm D, Sua D (2020) Privacy-preserving based task allocation with mobile edge clouds. Inf Sci 507:288–297

    Article  Google Scholar 

  26. Wang M, He K, Chen J, Du R, Zhang B, Li Z (2022) Panda: lightweight non-interactive privacy-preserving data aggregation for constrained devices. Futur Gener Comput Syst 131:28–42

    Article  Google Scholar 

  27. Ren Y, Liu W, Liu A, Wang T, Li A (2022) A privacy-protected intelligent crowdsourcing application of iot based on the reinforcement learning. Futur Gener Comput Syst 127:56–69

    Article  Google Scholar 

  28. Shokri R (2011) Quantifying and protecting location privacy. Inf Technol 57(4):257–263

    Google Scholar 

  29. Zhang L, Ma CG, Yang ST, Zheng X (2017) Probability indistinguishable: a query and location correlation attack resistance scheme. Wireless Pers Commun 97:6167–6187

    Article  Google Scholar 

  30. Jing C, He K, Quan Y, Min C, Du R, Yang X (2018) Blind filtering at third parties: an efficient privacy-preserving framework for location-based services. IEEE Trans Mob Comput 17(11):2524–2535

    Article  Google Scholar 

  31. Torra V (2020) Fuzzy clustering-based microaggregation to achieve probabilistic k-anonymity for data with constraints. J Intell Fuzzy Syst 39(5):5999–6008

    Article  Google Scholar 

  32. Yan Y, Herman EA, Mahmood A, Feng T, Xie P (2021) A weighted k-member clustering algorithm for k-anonymization. Computing 103:2251–2273

    Article  MATH  Google Scholar 

  33. Mahdavifar S, Deldar F, Mahdikhani H (2021) Personalized privacy-preserving publication of trajectory data by generalization and distortion of moving points. J Netw Syst Manage 30:10

    Article  Google Scholar 

  34. Saurabh S, Shailendra R, Osama A, Amr T, Byungun Y (2022) A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Futur Gener Comput Syst 129:380–388

    Article  Google Scholar 

  35. Liu Y, Tian J, Du Y, Li S (2021) A random sensitive area based privacy preservation algorithm for location-based service. Wireless Pers Commun 119:1179–1192

    Article  Google Scholar 

  36. Rodroguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  37. Vashishtha G, Kumar R (2022) An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel. Measurement 187:110272

    Article  Google Scholar 

  38. Vashishtha G, Chauhan S, Kumar A, Kumar R (2022) An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects. Meas Sci Technol 33:075013

    Article  Google Scholar 

  39. Vashishtha G (2022) Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine. Meas Sci Technol 33(1):015006

    Article  Google Scholar 

  40. Vashishtha G, Kumar R (2022) Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals. Eng Res Exp 3(3):035041

    Article  Google Scholar 

  41. Vashishtha G, Kumar R (2021) an effective health indicator for Pelton wheel using Levy Flight mutated Genetic Algorithm. Meas Sci Technol 32(9):094003

    Article  Google Scholar 

  42. Vashishtha G, Chauhan S, Yadav N, Kumar A, and Kumar R. Adaptive momeda model based variational mode decomposition for pelton wheel fault detection, In Proc. 2021 International Conference on Simulation, Automation and Smart Manufacturing (SASM), 2022

  43. Chauhan S, Vashishtha G, Kumar A (2022) A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem. J Supercomput 78(5):6234–6274

    Article  Google Scholar 

  44. Vashishtha G, Chauhan S, Singh M, Kumar R (2022) Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm. Measurement 178:109389

    Article  Google Scholar 

  45. Vashishtha G, Kumar R (2021) Pelton wheel bucket fault diagnosis using improved Shannon entropy and expectation maximization principal component analysis. J Vib Eng Technol 10:335–349

    Article  Google Scholar 

  46. Chauhan S, Singh M, Aggarwal AK (2021) Cluster head selection in heterogeneous wireless sensor network using a new evolutionary algorithm. Wireless Pers Commun 119(1):585–616

    Article  Google Scholar 

  47. Qiu C, Squicciarini AC, Pang C, Wang N, Wu B (2020) Location privacy protection in vehicle-based spatial crowdsourcing via geo-indistinguishability. IEEE Trans Mob Comput 21(7):2436–2450

    Google Scholar 

  48. Huang C, Molisch AF, Geng YA, He R, Ai B, Zhong Z (2020) Trajectory-joint clustering algorithm for time-varying channel modeling. IEEE Trans Veh Technol 69(1):1041–1045

    Article  Google Scholar 

  49. Machanavajjhala A, Gehrke J, Kifer D, and Venkitasubramaniam M. l-diversity: Privacy beyond k-anonymity, In Proc. 22nd Intnl. Conf. Data Engg (ICDE), 2006

  50. Wong RC, Li J, Fu AW, Wang K (2006) (\(\alpha\), k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 33:754–759

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Acknowledgements

The authors would like to thank the National Natural Science Foundation of China (No.61902069, U1905211) and the Natural Science Foundation of Fujian Province of China (2021J011068).

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Correspondence to Jing Zhang.

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Zhang, J., Huang, Q., Hu, JY. et al. Dimension-aware under spatiotemporal constraints: an efficient privacy-preserving framework with peak density clustering. J Supercomput 79, 4164–4191 (2023). https://doi.org/10.1007/s11227-022-04826-4

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