{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T14:23:05Z","timestamp":1763389385114,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972148"],"award-info":[{"award-number":["61972148"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"crossref","award":["4182060"],"award-info":[{"award-number":["4182060"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019MS020"],"award-info":[{"award-number":["2019MS020"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.<\/jats:p>","DOI":"10.1186\/s13677-020-00225-3","type":"journal-article","created":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T09:02:51Z","timestamp":1611046971000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A differentially private distributed data mining scheme with high efficiency for edge computing"],"prefix":"10.1186","volume":"10","author":[{"given":"Xianwen","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruzhi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longfei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0901-8621","authenticated-orcid":false,"given":"Zhitao","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"issue":"1","key":"225_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s13677-018-0118-3","volume":"7","author":"G Santos","year":"2018","unstructured":"Santos G, Takako Endo P, da Silva Lisboa MF, da Silva LG, Sadok D, Kelner J, Lynn T (2018) Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. J Cloud Comp 7(1):16","journal-title":"J Cloud Comp"},{"key":"225_CR2","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.future.2018.03.054","volume":"86","author":"M Chen","year":"2018","unstructured":"Chen M, Li W, Hao Y, Qian Y, Humar I (2018) Edge cognitive computing based smart healthcare system. Futur Gener Comput Syst 86:403\u2013411","journal-title":"Futur Gener Comput Syst"},{"issue":"1","key":"225_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-015-0498-8","volume":"2019","author":"H Liu","year":"2019","unstructured":"Liu H, Kou H, Yan C, Qi L (2019) Link prediction in paper citation network to construct paper correlation graph. EURASIP J Wirel Commun Netw 2019(1):1\u201312","journal-title":"EURASIP J Wirel Commun Netw"},{"key":"225_CR4","first-page":"1","volume-title":"IEEE transactions on intelligent transportation systems","author":"C Chen","year":"2020","unstructured":"Chen C, Liu Z, Wan S, Luan J, Pei Q (2020) Traffic flow prediction based on deep learning in internet of vehicles. In: IEEE transactions on intelligent transportation systems, pp 1\u201314"},{"key":"225_CR5","doi-asserted-by":"publisher","unstructured":"Wan S, Xu X, Wang T, Gu T (2020) An intelligent video analysis method for abnormal event detection in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 1\u20139. https:\/\/doi.org\/10.1109\/TITS.2020.3017505","DOI":"10.1109\/TITS.2020.3017505"},{"issue":"1","key":"225_CR6","first-page":"1","volume":"52","author":"Centers for Disease Control and Prevention","year":"2003","unstructured":"Centers for Disease Control and Prevention (2003) HIPAA privacy rule and public health. Guidance from CDC and the US Department of Health and Human Services. MMWR Morb Mortal Wkly Rep 52(1):1\u201317","journal-title":"MMWR Morb Mortal Wkly Rep"},{"key":"225_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.jpdc.2020.08.012","volume":"147","author":"Z Guan","year":"2021","unstructured":"Guan Z, Lu X, Yang W, Wu L, Wang N, Zhang Z (2021) Achieving efficient and privacy-preserving energy trading based on blockchain and ABE in smart grid. J Parallel Distribut Comput 147:34\u201345","journal-title":"J Parallel Distribut Comput"},{"key":"225_CR8","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.comcom.2020.04.018","volume":"157","author":"W Zhong","year":"2020","unstructured":"Zhong W, Yin X, Zhang X, Li S, Dou W, Wang R, Qi L (2020) Multi-dimensional quality-driven service recommendation with privacy-preservation in Mobile edge environment. Comput Commun 157:116\u2013123","journal-title":"Comput Commun"},{"key":"225_CR9","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/j.ins.2019.11.013","volume":"514","author":"Z Guan","year":"2020","unstructured":"Guan Z, Liu X, Wu L, Xu R, Zhang J, Li Y (2020) Cross-lingual multi-keyword rank search with semantic extension over encrypted data. Inf Sci 514:523\u2013540","journal-title":"Inf Sci"},{"issue":"2","key":"225_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. Transact Intell Syst Technol 10(2):1\u201319","journal-title":"Transact Intell Syst Technol"},{"key":"225_CR11","first-page":"1","volume-title":"International conference on theory and applications of models of computation","author":"C Dwork","year":"2008","unstructured":"Dwork C (2008) Differential privacy: a survey of results. International conference on theory and applications of models of computation. Springer, Switzerland, pp 1\u201319"},{"issue":"4","key":"225_CR12","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1109\/TNSE.2020.2985096","volume":"7","author":"Z Guan","year":"2020","unstructured":"Guan Z, Lv Z, Sun X, Wu L, Wu J, Du X, Guizani M (2020) A differentially private big data nonparametric Bayesian clustering algorithm in smart grid. IEEE Trans Netw Sci Eng 7(4):2631\u20132641","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"225_CR13","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1145\/1835804.1835868","volume-title":"16th ACM Sigkdd international conference on knowledge discovery and data mining","author":"A Friedman","year":"2010","unstructured":"Friedman A, Schuster A (2010) Data mining with differential privacy. 16th ACM Sigkdd international conference on knowledge discovery and data mining. ACM, Washington, DC, pp 493\u2013502"},{"key":"225_CR14","first-page":"385","volume-title":"International Conference on Data Mining","author":"NH Phan","year":"2017","unstructured":"Phan NH, Wu X, Hu H, Dou D (2017) Adaptive Laplace mechanism: differential privacy preservation in deep learning. International Conference on Data Mining. IEEE, New York, pp 385\u2013394"},{"key":"225_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TBDATA.2020.2975587","volume-title":"IEEE Transactions on Big Data","author":"L Qi","year":"2020","unstructured":"Qi L, He Q, Chen F, Zhang X, Dou W, Ni Q (2020) Data-driven web APIs recommendation for building web applications. In: IEEE Transactions on Big Data, p 1. https:\/\/doi.org\/10.1109\/TBDATA.2020.2975587"},{"key":"225_CR16","doi-asserted-by":"crossref","unstructured":"Zhao L, Ni L, Hu S, Chen Y, Zhou P, Xiao F (2018) Inprivate digging: enabling tree-based distributed data mining with differential privacy. In: IEEE International Conference on Computer Communications. IEEE, pp 2087\u20132095","DOI":"10.1109\/INFOCOM.2018.8486352"},{"issue":"1","key":"225_CR17","first-page":"64","volume":"8","author":"CC Aggarwal","year":"2008","unstructured":"Aggarwal CC, Yu PS (2008) A general survey of privacy-preserving data mining models and algorithms. J Vasc Surg 8(1):64\u201370","journal-title":"J Vasc Surg"},{"key":"225_CR18","first-page":"1","volume":"76","author":"P Zhao","year":"2019","unstructured":"Zhao P, Zhang G, Wan S, Liu G, Umer T (2019) A survey of local differential privacy for securing internet of vehicles. J Supercomput 76:1\u201322","journal-title":"J Supercomput"},{"issue":"1","key":"225_CR19","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106","journal-title":"Mach Learn"},{"issue":"1\u20133","key":"225_CR20","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1\u20133):37\u201352","journal-title":"Chemom Intell Lab Syst"},{"issue":"1","key":"225_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"IA Basheer","year":"2000","unstructured":"Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3\u201331","journal-title":"J Microbiol Methods"},{"key":"225_CR22","doi-asserted-by":"crossref","unstructured":"Blum A, Dwork C, Mcsherry F, Nissim K (2005) Practical Privacy: the SuLQ Framework. In: 24th ACM Sigmod-Sigact-Sigart Symposium on Principles of Database Systems. ACM, pp 128\u2013138","DOI":"10.1145\/1065167.1065184"},{"key":"225_CR23","doi-asserted-by":"crossref","unstructured":"Rana S, Gupta SK, Venkatesh S (2015) Differentially private random forest with high utility. In: IEEE International Conference on Data Mining. IEEE, pp 955\u2013960","DOI":"10.1109\/ICDM.2015.76"},{"key":"225_CR24","first-page":"192","volume-title":"Australasian Joint Conference on Artificial Intelligence","author":"S Fletcher","year":"2015","unstructured":"Fletcher S, Islam MZ (2015) A differentially private random decision forest using reliable signal-to-noise ratios. In: Australasian Joint Conference on Artificial Intelligence, pp 192\u2013203"},{"key":"225_CR25","unstructured":"Huang Y, Evans D, Katz J, Malka L (2011) Faster secure two-party computation using garbled circuits. In: Usenix Conference on Security. USENIX Association, pp 331\u2013335"},{"issue":"4","key":"225_CR26","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s00778-010-0214-6","volume":"20","author":"N Mohammed","year":"2011","unstructured":"Mohammed N, Fung BCM, Debbabi M (2011) Anonymity meets game theory: secure data integration with malicious participants. VLDB J 20(4):567\u2013588","journal-title":"VLDB J"},{"issue":"3","key":"225_CR27","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1136\/amiajnl-2012-001027","volume":"20","author":"N Mohammed","year":"2013","unstructured":"Mohammed N, Jiang X, Chen R, Fung B, Ohno-Machado L (2013) Privacy-preserving heterogeneous health data sharing. J Am Med Inform Assoc 20(3):462\u2013469","journal-title":"J Am Med Inform Assoc"},{"issue":"10","key":"225_CR28","doi-asserted-by":"publisher","first-page":"2520","DOI":"10.1109\/TKDE.2013.18","volume":"26","author":"S Goryczka","year":"2013","unstructured":"Goryczka S, Xiong L, Fung BCM (2013) m-privacy for collaborative data publishing. IEEE Trans Knowl Data Eng 26(10):2520\u20132533","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"225_CR29","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.datak.2007.02.004","volume":"63","author":"F Emek\u00e7i","year":"2007","unstructured":"Emek\u00e7i F, Sahin OD, Agrawal D, Abbadi E (2007) Privacy preserving decision tree learning over multiple parties. Data Knowl Eng 63(2):348\u2013361","journal-title":"Data Knowl Eng"},{"issue":"1","key":"225_CR30","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10618-006-0051-9","volume":"14","author":"S Gambs","year":"2007","unstructured":"Gambs S, K\u00e9gl B, A\u00efmeur E (2007) Privacy-preserving boosting. Data Mining Knowl Discov 14(1):131\u2013170","journal-title":"Data Mining Knowl Discov"},{"key":"225_CR31","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of online learning and an application to boosting. In: European Conference on Computational Learning Theory. Springer, pp 23\u201337"},{"key":"225_CR32","doi-asserted-by":"publisher","first-page":"101930","DOI":"10.1016\/j.cose.2020.101930","volume":"96","author":"Z Guan","year":"2020","unstructured":"Guan Z, Sun X, Shi L, Wu L, Du X (2020) A differentially private greedy decision forest classification algorithm with high utility. Comput Sec 96:101930","journal-title":"Comput Sec"},{"key":"225_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2020.101522","volume":"92","author":"J Li","year":"2020","unstructured":"Li J, Cai T, Deng K, Wang X, Sellis T, Xia F (2020) Community-diversified influence maximization in social networks. Inf Syst 92:1\u201312","journal-title":"Inf Syst"},{"issue":"4","key":"225_CR34","first-page":"e1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdisc Rev 8(4):e1249","journal-title":"Wiley Interdisc Rev"},{"issue":"11","key":"225_CR35","first-page":"1639","volume":"1","author":"B Amarnath","year":"2016","unstructured":"Amarnath B, Balamurugan S, Alias A (2016) Review on feature selection techniques and its impact for effective data classification using UCI machine learning repository dataset. J Eng Sci Technol 1(11):1639\u20131646","journal-title":"J Eng Sci Technol"},{"key":"225_CR36","unstructured":"J. Prince. Social science research on pornography, http:\/\/byuresearch.org\/ssrp"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-020-00225-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13677-020-00225-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-020-00225-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T09:43:16Z","timestamp":1611049396000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-020-00225-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,19]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["225"],"URL":"https:\/\/doi.org\/10.1186\/s13677-020-00225-3","relation":{},"ISSN":["2192-113X"],"issn-type":[{"type":"electronic","value":"2192-113X"}],"subject":[],"published":{"date-parts":[[2021,1,19]]},"assertion":[{"value":"30 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that there is no conflict of interests regarding the publication of this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7"}}