{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:56:41Z","timestamp":1754157401967,"version":"3.41.2"},"reference-count":22,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2012,8,17]],"date-time":"2012-08-17T00:00:00Z","timestamp":1345161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,8,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to provide a privacy preserving method based on grey model and apply it to clustering, so that the privacy of the individuals can be protected while the information loss is kept low.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>GM(1,1) model is utilized reversely in the approach to add noise to the original data, so as to make use of the grey information to blur the true one.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>It is shown that the privacy preserving method based on grey model can achieve both high effectiveness and high efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper presents the first attempt to apply the grey model to protect data privacy. The experimental results show the effectiveness and the efficiency of the proposed method.<\/jats:p><\/jats:sec>","DOI":"10.1108\/20439371211260135","type":"journal-article","created":{"date-parts":[[2014,11,13]],"date-time":"2014-11-13T12:10:36Z","timestamp":1415880636000},"page":"157-165","source":"Crossref","is-referenced-by-count":7,"title":["Privacy preserving method based on GM(1,1) and its application to clustering"],"prefix":"10.1108","volume":"2","author":[{"given":"Kun","family":"Guo","sequence":"first","affiliation":[]},{"given":"Qishan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022030820372715800_b15","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. and Yu, P.S. (2008), Privacy Preserving Data Mining: Models and Algorithms, Springer, New York, NY.","DOI":"10.1007\/978-0-387-70992-5"},{"key":"key2022030820372715800_b2","doi-asserted-by":"crossref","unstructured":"Agrawal, C. and Aggarwal, C.C. (2002), \u201cOn the design and quantification of privacy preserving data mining algorithms\u201d, Proceedings of the 21th ACM SIGMOD\u2010SIGACT\u2010SIGART Symposium on Principles of Database Systems, Madison, WI, USA, pp. 247\u201055.","DOI":"10.1145\/375551.375602"},{"key":"key2022030820372715800_b3","doi-asserted-by":"crossref","unstructured":"Agrawal, R. and Srikant, R. (2000), \u201cPrivacy\u2010preserving data mining\u201d, Proceedings of ACM SIGMOD Conference, Dallas, TX, USA, pp. 439\u201050.","DOI":"10.1145\/335191.335438"},{"key":"key2022030820372715800_b21","unstructured":"Brand, R., Domingo\u2010ferrer, J. and Mateo\u2010sanz, J.M. (2002), \u201cReference data sets to test and compare SDC methods for protection of numerical microdata\u201d, available at: http:\/\/neon.vb.cbs.nl\/casc."},{"key":"key2022030820372715800_b19","doi-asserted-by":"crossref","unstructured":"Chen, K., Sun, G. and Liu, L. (2007), \u201cTowards attack\u2010resilient geometric data perturbation\u201d, Proceedings of the Seventh SIAM International Conference on Data Mining, Minneapolis, MN, USA.","DOI":"10.1137\/1.9781611972771.8"},{"key":"key2022030820372715800_b9","unstructured":"Deng, J.L. (2002), Elements on Grey Theory, Huazhong University of Science and Technology Press, Wuhan."},{"key":"key2022030820372715800_b20","unstructured":"Frank, A. and Asuncion, A. (2010), \u201cUCI machine learning repository\u201d, available at: http:\/\/archive.ics.uci.edu\/ml."},{"key":"key2022030820372715800_b8","doi-asserted-by":"crossref","unstructured":"Hajian, S. and Azgomi, M.A. (2008), \u201cA privacy preserving clustering technique using Haar wavelet transform and scaling data perturbation\u201d, International Conference on Innovations in Information Technology, Al Ain, United Arab Emirates, pp. 218\u201022.","DOI":"10.1109\/INNOVATIONS.2008.4781665"},{"key":"key2022030820372715800_b17","doi-asserted-by":"crossref","unstructured":"Huang, Z., Du, W. and Chen, B. (2005), \u201cDeriving private information from randomized data\u201d, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, Baltimore, MD, USA, pp. 37\u201048.","DOI":"10.1145\/1066157.1066163"},{"key":"key2022030820372715800_b18","unstructured":"Kargupta, H., Datta, S., Wang, Q. and Sivakumar, K. (2003), \u201cOn the privacy preserving properties of random data perturbation techniques\u201d, Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL, USA, pp. 99\u2010106."},{"key":"key2022030820372715800_b11","unstructured":"Liu, H. and Zhang, Q.S. (2007), \u201cLife prediction of mechanical products of GM(1,1) based on particle swarm optimization\u201d, Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China, pp. 409\u201013."},{"key":"key2022030820372715800_b12","unstructured":"Liu, H. and Zhang, Q.S. (2008), \u201cGM(2, 1, \u03bb, \u03c1) based on particle swarm optimization\u201d, System Engineering \u2013 Theory & Practice, Vol. 28, pp. 96\u2010101."},{"key":"key2022030820372715800_b7","doi-asserted-by":"crossref","unstructured":"Mukherjee, S., Chen, Z. and Gangopadhyay, A. (2006), \u201cA privacy\u2010preserving technique for Euclidean distance\u2010based mining algorithms using Fourier\u2010related transforms\u201d, The VLDB Journal, Vol. 15, pp. 293\u2010315.","DOI":"10.1007\/s00778-006-0010-5"},{"key":"key2022030820372715800_b16","doi-asserted-by":"crossref","unstructured":"Muralidhar, K., Parsa, R. and Sarathy, R. (1999), \u201cA general additive data perturbation method for database security\u201d, Management Science, Vol. 45, pp. 1399\u2010415.","DOI":"10.1287\/mnsc.45.10.1399"},{"key":"key2022030820372715800_b4","unstructured":"Oliveira, S.R.M. and Zaiane, O.R. (2003), \u201cPrivacy preserving clustering by data transformation\u201d, Proceedings of the 18th Brazilian Symposium on Databases, pp. 304\u201018."},{"key":"key2022030820372715800_b5","unstructured":"Oliveira, S.R.M. and Zaiane, O.R. (2004), \u201cData perturbation by rotation for privacy\u2010preserving clustering\u201d, Technical Report TR04\u201017, Edmonton, Canada, Department of Computing Science, University of Alberta, Edmonton."},{"key":"key2022030820372715800_b6","doi-asserted-by":"crossref","unstructured":"Oliveira, S.R.M. and Zaiane, O.R. (2007), \u201cA privacy\u2010preserving clustering approach toward secure and effective data analysis for business collaboration\u201d, Computers & Security, Vol. 26, pp. 81\u201093.","DOI":"10.1016\/j.cose.2006.08.003"},{"key":"key2022030820372715800_b10","unstructured":"Zhang, Q.S. (2002), Difference Information Theory of Grey Hazy Set, Petroleum Industry Press, Beijing."},{"key":"key2022030820372715800_b14","doi-asserted-by":"crossref","unstructured":"Zhang, Q.S. and Chen, K.J. (2008), \u201cGrey sets and their greyness measure\u201d, Proceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore, pp. 2045\u20108.","DOI":"10.1109\/ICSMC.2008.4811592"},{"key":"key2022030820372715800_b13","unstructured":"Zhang, Q.S. and Wang, H.Y. (2009), \u201cMeasuring the greyness of grey cluster knowledge\u201d, The Journal of Grey System, Vol. 21, pp. 259\u201068."},{"key":"key2022030820372715800_b22","unstructured":"Zhang, G.L. and Yin, J. (2006), \u201cPrivacy preserving clustering by isometric transformation\u201d, Application Research of Computers, Vol. 23, pp. 83\u20106."},{"key":"key2022030820372715800_b1","doi-asserted-by":"crossref","unstructured":"Zhou, S.G., Li, F., Tao, Y.F. and Xiao, X.K. (2009), \u201cPrivacy preservation in database applications: a survey\u201d, Chinese Journal of Computers, Vol. 32, pp. 847\u201061.","DOI":"10.3724\/SP.J.1016.2009.00847"}],"container-title":["Grey Systems: Theory and Application"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/20439371211260135","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/20439371211260135\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/20439371211260135\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:52:38Z","timestamp":1753401158000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/gs\/article\/2\/2\/157-165\/79070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,8,17]]},"references-count":22,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2012,8,17]]}},"alternative-id":["10.1108\/20439371211260135"],"URL":"https:\/\/doi.org\/10.1108\/20439371211260135","relation":{},"ISSN":["2043-9377"],"issn-type":[{"type":"print","value":"2043-9377"}],"subject":[],"published":{"date-parts":[[2012,8,17]]}}}