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 (k, l)-privacy protection algorithm ((k, l)-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.















Data availability statement
Data will be made available on reasonable request.
References
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
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
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
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
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
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
Wei YC, Wu WC, Lai GH, Chu YC (2020) pISRA: privacy considered information security risk assessment model. J Supercomput 76:1468–1481
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
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
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
Akremi A, Rouached M (2021) A comprehensive and holistic knowledge model for cloud privacy protection. J Supercomput 77:7956–7988
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
Bostanipour B, Theodorakopoulos G (2021) Joint obfuscation of location and its semantic information for privacy protection. Comput Secur 107(4):102310
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
Goncalves C, Bessa RJ, Pinson P (2021) Privacy-preserving distributed learning for renewable energy forecasting. IEEE Trans Sustain Energy 12(3):1777–1787
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
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
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
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
Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertainty, Fuzziness Knowl-Based Syst 10(5):557–570
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
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
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)
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
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
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
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
Shokri R (2011) Quantifying and protecting location privacy. Inf Technol 57(4):257–263
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
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
Torra V (2020) Fuzzy clustering-based microaggregation to achieve probabilistic k-anonymity for data with constraints. J Intell Fuzzy Syst 39(5):5999–6008
Yan Y, Herman EA, Mahmood A, Feng T, Xie P (2021) A weighted k-member clustering algorithm for k-anonymization. Computing 103:2251–2273
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
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
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
Rodroguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496
Vashishtha G, Kumar R (2022) An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel. Measurement 187:110272
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
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
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
Vashishtha G, Kumar R (2021) an effective health indicator for Pelton wheel using Levy Flight mutated Genetic Algorithm. Meas Sci Technol 32(9):094003
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
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
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
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
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
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
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
Machanavajjhala A, Gehrke J, Kifer D, and Venkitasubramaniam M. l-diversity: Privacy beyond k-anonymity, In Proc. 22nd Intnl. Conf. Data Engg (ICDE), 2006
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
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-022-04826-4