{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:09:40Z","timestamp":1743091780263,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030243104"},{"type":"electronic","value":"9783030243111"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-24311-1_40","type":"book-chapter","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T12:02:51Z","timestamp":1561723371000},"page":"557-572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering Data in Secured, Distributed Datasets"],"prefix":"10.1007","author":[{"given":"Sayantan","family":"Dey","sequence":"first","affiliation":[]},{"given":"Lee A.","family":"Carraher","sequence":"additional","affiliation":[]},{"given":"Anindya","family":"Moitra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6562-8646","authenticated-orcid":false,"given":"Philip A.","family":"Wilsey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,29]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Achlioptas, D.: Database-friendly random projections. In: Proceedings of the 20th Symposium on Principles of Database Systems, pp. 274\u2013281 (2001)","DOI":"10.1145\/375551.375608"},{"key":"40_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-70992-5","volume-title":"Privacy-Preserving Data Mining: Models and Algorithms","year":"2008","unstructured":"Aggarwal, C.C., Yu, P.S. (eds.): Privacy-Preserving Data Mining: Models and Algorithms. Springer, Boston (2008). https:\/\/doi.org\/10.1007\/978-0-387-70992-5"},{"issue":"5","key":"40_CR3","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1109\/26.494294","volume":"44","author":"O Amrani","year":"1996","unstructured":"Amrani, O., Be\u2019ery, Y.: Efficient bounded-distance decoding of the hexacode and associated decoders for the Leech lattice and the Golay code. IEEE Trans. Commun. 44(5), 534\u2013537 (1996)","journal-title":"IEEE Trans. Commun."},{"issue":"4","key":"40_CR4","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1109\/18.335970","volume":"40","author":"O Amrani","year":"1994","unstructured":"Amrani, O., Be\u2019ery, Y., Vardy, A., Sun, F.W., van Tilborg, H.C.A.: The Leech lattice and the Golay code: bounded-distance decoding and multilevel constructions. IEEE Trans. Inf. Theory 40(4), 1030\u20131043 (1994)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"40_CR5","unstructured":"Andoni, A.: Nearest neighbor search: the old, the new, and the impossible. Ph.D. thesis, Massachusetts Institute of Technology, September (2009)"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 47th Annual IEEE Symposium on Foundations of Computer Science. FOCS 2006, pp. 459\u2013468 (2006)","DOI":"10.1109\/FOCS.2006.49"},{"key":"40_CR7","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: UCI machine learning repository (2012). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Human+Activity+Recognition+Using+Smartphones"},{"key":"40_CR8","unstructured":"Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027\u20131035. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2007), http:\/\/dl.acm.org\/citation.cfm?id=1283383.1283494"},{"volume-title":"Adaptive Control Processes: A Guided Tour","year":"1961","key":"40_CR9","unstructured":"Bellman, R.E. (ed.): Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)"},{"issue":"15","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1002\/cpe.1761","volume":"23","author":"M Cafaro","year":"2011","unstructured":"Cafaro, M., Tempesta, P.: Finding frequent items in parallel. Concurr. Comput.: Pract. Exper. 23(15), 1774\u20131788 (2011). https:\/\/doi.org\/10.1002\/cpe.1761","journal-title":"Concurr. Comput.: Pract. Exper."},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Carraher, L.A., Wilsey, P.A., Moitra, A., Dey, S.: Multi-probe random projection clustering to secure very large distributed datasets. In: 2nd International Workshop on Privacy and Security of Big Data, October (2015)","DOI":"10.1109\/BigData.2015.7363964"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Carraher, L.A., Wilsey, P.A., Moitra, A., Dey, S.: Random projection clustering on streaming data. In: IEEE 16th International Conference on Data Mining Workshops (ICDMW), December, pp. 708\u2013715 (2016)","DOI":"10.1109\/ICDMW.2016.0105"},{"key":"40_CR13","doi-asserted-by":"publisher","unstructured":"Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the twentieth annual symposium on Computational geometry, SCG 2004, pp. 253\u2013262. ACM, New York (2004). https:\/\/doi.org\/10.1145\/997817.997857","DOI":"10.1145\/997817.997857"},{"key":"40_CR14","unstructured":"Davis, K.A., Owusu, E.B.: UCI machine learning repository (2016). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Smartphone+Dataset+for+Human+Activity+Recognition+%28HAR%29+in+Ambient+Assisted+Living+%28AAL%29"},{"issue":"1","key":"40_CR15","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107\u2013113 (2008)","journal-title":"Commun. ACM"},{"key":"40_CR16","unstructured":"Fiorini, S.: UCI machine learning repository (2016). https:\/\/archive.ics.uci.edu\/ml\/datasets\/gene+expression+cancer+RNA-Seq"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Franklin, J., Wenke, S., Quasem, S., Carraher, L.A., Wilsey, P.A.: streamingRPHash: random projection clustering of high-dimensional data in a mapreduce framework. In: IEEE Cluster 2016, September (2016). (poster)","DOI":"10.1109\/CLUSTER.2016.89"},{"issue":"1","key":"40_CR18","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. JSTOR: Appl. Stat. 28(1), 100\u2013108 (1979)","journal-title":"JSTOR: Appl. Stat."},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Herrero, J., Valencia, A., Dopazo, J.: A hierarchical unsupervised growing neural network for clustering gene expression patterns (2001)","DOI":"10.1093\/bioinformatics\/17.2.126"},{"key":"40_CR20","unstructured":"Health insurance portability and accountability act (2004). http:\/\/www.hhs.gov\/ocr\/hipaa\/"},{"issue":"1","key":"40_CR21","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193\u2013218 (1985)","journal-title":"J. Classif."},{"key":"40_CR22","doi-asserted-by":"publisher","unstructured":"Jagannathan, G., Pillaipakkamnatt, K., Wright, R.N.: A new privacy-preserving distributed k-clustering algorithm. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 494\u2013498 (2006). https:\/\/doi.org\/10.1137\/1.9781611972764.47","DOI":"10.1137\/1.9781611972764.47"},{"key":"40_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/11430919_51","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"H-P Kriegel","year":"2005","unstructured":"Kriegel, H.-P., Kunath, P., Pfeifle, M., Renz, M.: Approximated clustering of distributed high-dimensional data. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 432\u2013441. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11430919_51"},{"key":"40_CR24","doi-asserted-by":"publisher","unstructured":"Liu, B., Xia, Y., Yu, P.S.: Clustering through decision tree construction. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, CIKM 2000, pp. 20\u201329. ACM, New York (2000). https:\/\/doi.org\/10.1145\/354756.354775","DOI":"10.1145\/354756.354775"},{"key":"40_CR25","unstructured":"Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.: cluster: Cluster Analysis Basics and Extensions (2013). (r package version 1.14.4)"},{"key":"40_CR26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071","volume-title":"Introduction to Information Retrieval","author":"CD Manning","year":"2008","unstructured":"Manning, C.D., Raghavan, P., Sch\u00fctze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)"},{"issue":"3","key":"40_CR27","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/s00357-014-9161-z","volume":"31","author":"F Murtagh","year":"2014","unstructured":"Murtagh, F., Legendre, P.: Ward\u2019s hierarchical agglomerative clustering method: which algorithms implement Ward\u2019s criterion? J. Classif. 31(3), 274\u2013295 (2014). https:\/\/doi.org\/10.1007\/s00357-014-9161-z","journal-title":"J. Classif."},{"key":"40_CR28","unstructured":"Reyes-Ortiz, J.L., Oneto, L., Sam, A., Parra, X., Anguita, D.: UCI machine learning repository (2015). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions"},{"issue":"4","key":"40_CR29","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1109\/18.391252","volume":"41","author":"FW Sun","year":"1995","unstructured":"Sun, F.W., van Tilborg, H.C.A.: The Leech lattice, the octacode, and decoding algorithms. IEEE Trans. Inf. Theory 41(4), 1097\u20131106 (1995)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Terasawa, K., Tanaka, Y.: Spherical LSH for approximate nearest neighbor search on unit hypersphere. In: WADS, pp. 27\u201338 (2007)","DOI":"10.1007\/978-3-540-73951-7_4"},{"key":"40_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-540-73435-2_9","volume-title":"Advances in Data Mining. Theoretical Aspects and Applications","author":"T Urruty","year":"2007","unstructured":"Urruty, T., Djeraba, C., Simovici, D.A.: Clustering by random projections. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 107\u2013119. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-73435-2_9"},{"key":"40_CR32","doi-asserted-by":"publisher","unstructured":"Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 639\u2013644. ACM, New York (2002). https:\/\/doi.org\/10.1145\/775047.775142","DOI":"10.1145\/775047.775142"},{"issue":"5","key":"40_CR33","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1109\/18.412695","volume":"41","author":"A Vardy","year":"1995","unstructured":"Vardy, A.: Even more efficient bounded-distance decoding of the hexacode, the Golay code, and the Leech lattice. IEEE Trans. Inf. Theory 41(5), 1495\u20131499 (1995)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"40_CR34","series-title":"DIMACS Series","volume-title":"The Random Projection Method","author":"SS Vempala","year":"2004","unstructured":"Vempala, S.S.: The Random Projection Method. DIMACS Series. American Mathematical Society, Providence (2004)"},{"key":"40_CR35","unstructured":"Vergara, A., Fonollosa, J., Rodriguez-Lujan, I., Huerta, R.: UCI machine learning repository (2013). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations"},{"key":"40_CR36","volume-title":"Hadoop: The Definitive Guide","author":"T White","year":"2009","unstructured":"White, T.: Hadoop: The Definitive Guide. O\u2019Reilly Media Inc., Sebastopol (2009)"},{"issue":"11","key":"40_CR37","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2934664","volume":"59","author":"M Zaharia","year":"2016","unstructured":"Zaharia, M., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56\u201365 (2016). https:\/\/doi.org\/10.1145\/2934664","journal-title":"Commun. ACM"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2019"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-24311-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T07:20:24Z","timestamp":1611213624000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-24311-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030243104","9783030243111"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-24311-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"29 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Saint Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}