{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:10:16Z","timestamp":1776276616648,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Inf Syst Front"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: <jats:italic>context data<\/jats:italic>, <jats:italic>continuous data<\/jats:italic>, and <jats:italic>media data<\/jats:italic>. We first proposed a stream data anonymisation method based on <jats:italic>k<\/jats:italic>-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.<\/jats:p>","DOI":"10.1007\/s10796-021-10116-w","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T08:03:20Z","timestamp":1620720200000},"page":"2227-2238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation"],"prefix":"10.1007","volume":"26","author":[{"given":"Wang","family":"Ren","sequence":"first","affiliation":[]},{"given":"Xin","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Du","sequence":"additional","affiliation":[]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5663-7420","authenticated-orcid":false,"given":"Shancang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Geyong","family":"Min","sequence":"additional","affiliation":[]},{"given":"Zhiwei","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"10116_CR1","doi-asserted-by":"publisher","first-page":"100129","DOI":"10.1016\/j.jii.2020.100129","volume":"18","author":"G Aceto","year":"2020","unstructured":"Aceto, G., Persico, V., Pescap\u00e9, A. (2020). Industry 4.0 and health: internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration, 18, 100129.","journal-title":"Journal of Industrial Information Integration"},{"key":"10116_CR2","doi-asserted-by":"crossref","unstructured":"Amar, Y., Haddadi, H., Mortier, R. (2018). An information-theoretic approach to time-series data privacy. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems (pp. 1\u20136).","DOI":"10.1145\/3195258.3195261"},{"issue":"4","key":"10116_CR3","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1109\/TII.2014.2300753","volume":"10","author":"L Da Xu","year":"2014","unstructured":"Da Xu, L., He, W., Li, S. (2014). Internet of things in industries: a survey. IEEE Transactions on Industrial Informatics, 10(4), 2233.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10116_CR4","doi-asserted-by":"publisher","first-page":"85887","DOI":"10.1109\/ACCESS.2019.2925236","volume":"7","author":"F Deldar","year":"2019","unstructured":"Deldar, F., & Abadi, M. (2019). PDP-SAG: personalized privacy protection in moving objects databases by combining differential privacy and sensitive attribute generalization. IEEE Access, 7, 85887.","journal-title":"IEEE Access"},{"issue":"12","key":"10116_CR5","doi-asserted-by":"publisher","first-page":"3298","DOI":"10.1109\/TIFS.2019.2914832","volume":"14","author":"J Domingo-Ferrer","year":"2019","unstructured":"Domingo-Ferrer, J., Soria-Comas, J., Mulero-Vellido, R. (2019). Steered microaggregation as a unified primitive to anonymize data sets and data streams. IEEE Transactions on Information Forensics and Security, 14(12), 3298.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"2","key":"10116_CR6","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/TBDATA.2018.2829886","volume":"6","author":"M Du","year":"2020","unstructured":"Du, M., Wang, K., Xia, Z., Zhang, Y. (2020). Differential privacy preserving of training model in wireless big data with edge computing. IEEE Transactions on Big Data, 6(2), 283.","journal-title":"IEEE Transactions on Big Data"},{"issue":"4","key":"10116_CR7","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/MSEC.2020.2992821","volume":"18","author":"K El Emam","year":"2020","unstructured":"El Emam, K. (2020). Seven ways to evaluate the utility of synthetic data. IEEE Security Privacy, 18(4), 56.","journal-title":"IEEE Security Privacy"},{"issue":"12","key":"10116_CR8","doi-asserted-by":"publisher","first-page":"4777","DOI":"10.1109\/TIT.2007.909106","volume":"53","author":"A Faldum","year":"2007","unstructured":"Faldum, A. (2007). On the trustworthiness of error-correcting codes. IEEE Transactions on Information Theory, 53(12), 4777.","journal-title":"IEEE Transactions on Information Theory"},{"issue":"2","key":"10116_CR9","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/TKDE.2008.129","volume":"21","author":"A Gionis","year":"2009","unstructured":"Gionis, A., & Tassa, T. (2009). k-Anonymization with minimal loss of information. IEEE Transactions on Knowledge and Data Engineering, 21(2), 206.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"6","key":"10116_CR10","doi-asserted-by":"publisher","first-page":"1554","DOI":"10.1109\/TIFS.2018.2881730","volume":"14","author":"P Gope","year":"2019","unstructured":"Gope, P., & Sikdar, B. (2019). Lightweight and privacy-friendly spatial data aggregation for secure power supply and demand management in smart grids. IEEE Transactions on Information Forensics and Security, 14(6), 1554.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"3","key":"10116_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1080\/23270012.2020.1801529","volume":"7","author":"A Gorkhali","year":"2020","unstructured":"Gorkhali, A., Li, L., Shrestha, A. (2020). Blockchain: a literature review. Journal of Management Analytics, 7(3), 321.","journal-title":"Journal of Management Analytics"},{"issue":"2","key":"10116_CR12","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1109\/TNSM.2020.2982555","volume":"17","author":"H Huang","year":"2020","unstructured":"Huang, H., Zhang, D., Xiao, F., Wang, K., Gu, J., Wang, R. (2020). Privacy-preserving approach PBCN in social network with differential privacy. IEEE Transactions on Network and Service Management, 17(2), 931.","journal-title":"IEEE Transactions on Network and Service Management"},{"key":"10116_CR13","doi-asserted-by":"crossref","unstructured":"Khavkin, M., & Last, M. (2018). Preserving differential privacy and utility of non-stationary data streams. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 29\u201334).","DOI":"10.1109\/ICDMW.2018.00012"},{"key":"10116_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Ooi, B.C., Wang, W. (2008). Anonymizing streaming data for privacy protection. In 2008 IEEE 24th International Conference on Data Engineering (pp. 1367\u20131369).","DOI":"10.1109\/ICDE.2008.4497558"},{"issue":"4","key":"10116_CR15","doi-asserted-by":"publisher","first-page":"6487","DOI":"10.1109\/JIOT.2019.2906946","volume":"6","author":"S Li","year":"2019","unstructured":"Li, S., Choo, K.R., Sun, Q., Buchanan, W.J., Cao, J. (2019). IoT forensics: amazon echo as a use case. IEEE Internet of Things Journal, 6(4), 6487.","journal-title":"IEEE Internet of Things Journal"},{"issue":"2","key":"10116_CR16","doi-asserted-by":"publisher","first-page":"2299","DOI":"10.1109\/JIOT.2019.2906157","volume":"6","author":"S Li","year":"2019","unstructured":"Li, S., Zhao, S., Yang, P., Andriotis, P., Xu, L., Sun, Q. (2019). Distributed consensus algorithm for events detection in cyber-physical systems. IEEE Internet of Things Journal, 6(2), 2299.","journal-title":"IEEE Internet of Things Journal"},{"issue":"3","key":"10116_CR17","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1080\/23270012.2020.1802622","volume":"7","author":"Y Lu","year":"2020","unstructured":"Lu, Y., & Ning, X. (2020). A vision of 6G\u20135G\u2019s successor. Journal of Management Analytics, 7 (3), 301.","journal-title":"Journal of Management Analytics"},{"key":"10116_CR18","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ins.2019.01.054","volume":"527","author":"Y Ma","year":"2020","unstructured":"Ma, Y., Wu, Y., Li, J., Ge, J. (2020). APCN: a scalable architecture for balancing accountability and privacy in large-scale content-based networks. Information Sciences, 527, 511.","journal-title":"Information Sciences"},{"issue":"7","key":"10116_CR19","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","volume":"7","author":"PC Mahawaga Arachchige","year":"2020","unstructured":"Mahawaga Arachchige, P.C., Bertok, P., Khalil, I., Liu, D., Camtepe, S., Atiquzzaman, M. (2020). Local differential privacy for deep learning. IEEE Internet of Things Journal, 7(7), 5827.","journal-title":"IEEE Internet of Things Journal"},{"key":"10116_CR20","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Clegg, R.G., Cavallaro, A., Haddadi, H. (2019). Mobile sensor data anonymization. In Proceedings of the International Conference on Internet of Things Design and Implementation (pp. 49\u201358).","DOI":"10.1145\/3302505.3310068"},{"key":"10116_CR21","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1109\/ACCESS.2016.2557846","volume":"4","author":"N Neverova","year":"2016","unstructured":"Neverova, N., Wolf, C., Lacey, G., Fridman, L., Chandra, D., Barbello, B., Taylor, G. (2016). Learning human identity from motion patterns. IEEE Access, 4, 1810.","journal-title":"IEEE Access"},{"key":"10116_CR22","doi-asserted-by":"crossref","unstructured":"Otgonbayar, A., Pervez, Z., Dahal, K. (2016). Toward anonymizing IoT data streams via partitioning. In 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) (pp. 331\u2013336).","DOI":"10.1109\/MASS.2016.049"},{"key":"10116_CR23","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.procs.2018.01.097","volume":"127","author":"ZE Ouazzani","year":"2018","unstructured":"Ouazzani, Z.E., & Bakkali, H.E. (2018). A new technique ensuring privacy in big data: K-anonymity without prior value of the threshold k. Procedia Computer Science, 127, 52. https:\/\/doi.org\/10.1016\/j.procs.2018.01.097. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S187705091830108X. Proceedings of the First International Conference on Intelligent Computing in Data Sciences, ICDS2017.","journal-title":"Procedia Computer Science"},{"issue":"7","key":"10116_CR24","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TKDE.2015.2391098","volume":"27","author":"Z Pervaiz","year":"2015","unstructured":"Pervaiz, Z., Ghafoor, A., Aref, W.G. (2015). Precision-bounded access control using sliding-window query views for privacy-preserving data streams. IEEE Transactions on Knowledge and Data Engineering, 27 (7), 1992.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10116_CR25","doi-asserted-by":"crossref","unstructured":"Phan, N., Wu, X., Hu, H., Dou, D. (2017). Adaptive laplace mechanism: Differential privacy preservation in deep learning. In 2017 IEEE International Conference on Data Mining (ICDM) (pp. 385\u2013394): IEEE.","DOI":"10.1109\/ICDM.2017.48"},{"issue":"1","key":"10116_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-10933-3","volume":"10","author":"L Rocher","year":"2019","unstructured":"Rocher, L., Hendrickx, J.M., De Montjoye, Y.A. (2019). Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications, 10(1), 1.","journal-title":"Nature Communications"},{"issue":"7","key":"10116_CR27","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.3390\/s18072307","volume":"18","author":"Y Shi","year":"2018","unstructured":"Shi, Y., Zhang, Z., Chao, H.C., Shen, B. (2018). Data privacy protection based on micro aggregation with dynamic sensitive attribute updating. Sensors, 18(7), 2307.","journal-title":"Sensors"},{"issue":"6","key":"10116_CR28","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TIFS.2017.2663337","volume":"12","author":"J Soria-Comas","year":"2017","unstructured":"Soria-Comas, J., Domingo-Ferrer, J., S\u00e1nchez, D., Meg\u00edas, D. (2017). Individual differential privacy: a utility-preserving formulation of differential privacy guarantees. IEEE Transactions on Information Forensics and Security, 12(6), 1418.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"10116_CR29","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1109\/JBHI.2014.2360546","volume":"19","author":"R Somolinos","year":"2015","unstructured":"Somolinos, R., Mu\u00f1oz, A., Hernando, M.E., Pascual, M., C\u00e1ceres, J., S\u00e1nchez-de-Madariaga, R., Fragua, J.A., Serrano, P., Salvador, C.H. (2015). Service for the Pseudonymization of electronic healthcare records based on ISO\/EN 13606 for the secondary use of information. IEEE Journal of Biomedical and Health Informatics, 19, 1937.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"5","key":"10116_CR30","doi-asserted-by":"publisher","first-page":"8155","DOI":"10.1109\/JIOT.2019.2925825","volume":"6","author":"W Viriyasitavat","year":"2019","unstructured":"Viriyasitavat, W., Da Xu, L., Bi, Z., Hoonsopon, D. (2019). Blockchain technology for applications in internet of Thing\u2019s mapping from system design perspective. IEEE Internet of Things Journal, 6(5), 8155.","journal-title":"IEEE Internet of Things Journal"},{"issue":"12","key":"10116_CR31","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.1109\/LCOMM.2018.2876449","volume":"22","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Huang, M., Jin, Q., Ma, J. (2018). DP3: a differential privacy-based privacy-preserving indoor localization mechanism. IEEE Communications Letters, 22(12), 2547.","journal-title":"IEEE Communications Letters"},{"key":"10116_CR32","first-page":"1","volume":"2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T. , An, Y., Wang, K., Li, S. (2020). Wearable sensor-based human activity recognition using hybrid deep learning techniques. Security and Communication Networks, 2020, 1\u201312.","journal-title":"Security and Communication Networks"},{"key":"10116_CR33","doi-asserted-by":"publisher","first-page":"55432","DOI":"10.1109\/ACCESS.2019.2913648","volume":"7","author":"J Xiao","year":"2019","unstructured":"Xiao, J., Li, S., Xu, Q. (2019). Video-based evidence analysis and extraction in digital forensic investigation. IEEE Access, 7, 55432.","journal-title":"IEEE Access"},{"issue":"8","key":"10116_CR34","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1080\/00207543.2018.1444806","volume":"56","author":"LD Xu","year":"2018","unstructured":"Xu, L.D., Xu, E.L., Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941.","journal-title":"International Journal of Production Research"},{"key":"10116_CR35","unstructured":"Yang, Y., Huang, S., Huang, W., Chang, X. (2020). Privacy-preserving cost-sensitive learning IEEE Transactions on Neural Networks and Learning Systems, 1\u201312."},{"key":"10116_CR36","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.jpdc.2019.06.002","volume":"132","author":"Z Yao","year":"2019","unstructured":"Yao, Z., Ge, J., Wu, Y., Jian, L. (2019). A privacy preserved and credible network protocol. Journal of Parallel and Distributed Computing, 132, 150.","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"10116_CR37","doi-asserted-by":"publisher","first-page":"27156","DOI":"10.1109\/ACCESS.2018.2828398","volume":"6","author":"S Yaseen","year":"2018","unstructured":"Yaseen, S., Abbas, S.M.A., Anjum, A., Saba, T., Khan, A., Malik, S.U.R., Ahmad, N., Shahzad, B., Bashir, A.K. (2018). Improved generalization for secure data publishing. IEEE Access, 6, 27156.","journal-title":"IEEE Access"},{"key":"10116_CR38","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.jii.2018.12.002","volume":"15","author":"M Yli-Ojanper\u00e4","year":"2019","unstructured":"Yli-Ojanper\u00e4, M., Sierla, S., Papakonstantinou, N., Vyatkin, V. (2019). Adapting an agile manufacturing concept to the reference architecture model industry 4.0: a survey and case study. Journal of Industrial Information Integration, 15, 147.","journal-title":"Journal of Industrial Information Integration"},{"issue":"8","key":"10116_CR39","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1109\/TC.2014.2360516","volume":"64","author":"X Zhang","year":"2015","unstructured":"Zhang, X., Dou, W., Pei, J., Nepal, S., Yang, C., Liu, C., Chen, J. (2015). Proximity-aware local-recoding anonymization with MapReduce for scalable big data privacy preservation in cloud. IEEE Transactions on Computers, 64(8), 2293.","journal-title":"IEEE Transactions on Computers"},{"issue":"01","key":"10116_CR40","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1142\/S2424862219500192","volume":"5","author":"C Zhang","year":"2020","unstructured":"Zhang, C., & Chen, Y. (2020). A review of research relevant to the emerging industry trends: industry 4.0, IoT, blockchain, and business analytics. Journal of Industrial Integration and Management, 5(01), 165.","journal-title":"Journal of Industrial Integration and Management"},{"issue":"3","key":"10116_CR41","doi-asserted-by":"publisher","first-page":"2115","DOI":"10.1109\/TII.2019.2936825","volume":"16","author":"T Zhang","year":"2020","unstructured":"Zhang, T., Zhu, T., Xiong, P., Huo, H., Tari, Z., Zhou, W. (2020). Correlated differential privacy: feature selection in machine learning. IEEE Transactions on Industrial Informatics, 16(3), 2115.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"6","key":"10116_CR42","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1109\/TCSS.2019.2924054","volume":"6","author":"S Zhao","year":"2019","unstructured":"Zhao, S., Li, S., Yao, Y. (2019). Blockchain enabled industrial internet of things technology. IEEE Transactions on Computational Social Systems, 6(6), 1442.","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"10116_CR43","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.ins.2019.04.003","volume":"491","author":"R Zhou","year":"2019","unstructured":"Zhou, R., Zhang, X., Wang, X., Yang, G., Wang, H., Wu, Y. (2019). Privacy-preserving data search with fine-grained dynamic search right management in fog-assisted Internet of Things. Information Sciences, 491, 251.","journal-title":"Information Sciences"}],"container-title":["Information Systems Frontiers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10796-021-10116-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10796-021-10116-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10796-021-10116-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T02:02:54Z","timestamp":1740535374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10796-021-10116-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10116"],"URL":"https:\/\/doi.org\/10.1007\/s10796-021-10116-w","relation":{},"ISSN":["1387-3326","1572-9419"],"issn-type":[{"value":"1387-3326","type":"print"},{"value":"1572-9419","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,11]]},"assertion":[{"value":"4 February 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}