{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:39:23Z","timestamp":1789101563909,"version":"build-2803163510"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,16]],"date-time":"2018-07-16T00:00:00Z","timestamp":1531699200000},"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":["61772064"],"award-info":[{"award-number":["61772064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.<\/jats:p>","DOI":"10.3390\/s18072307","type":"journal-article","created":{"date-parts":[[2018,7,16]],"date-time":"2018-07-16T11:38:50Z","timestamp":1531741130000},"page":"2307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating"],"prefix":"10.3390","volume":"18","author":[{"given":"Yancheng","family":"Shi","sequence":"first","affiliation":[{"name":"Key Laboratory of Communication and Information Systems, School of Electronic and Information Engineering, Beijing Municipal Commission of Education, Beijing Jiao Tong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenjiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Communication and Information Systems, School of Electronic and Information Engineering, Beijing Municipal Commission of Education, Beijing Jiao Tong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han-Chieh","family":"Chao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China"},{"name":"College of Electrical Engineering & Computer Science, National Ilan University, Yilan City, Yilan County 260, Taiwan"},{"name":"Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Communication and Information Systems, School of Electronic and Information Engineering, Beijing Municipal Commission of Education, Beijing Jiao Tong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.compeleceng.2012.05.001","article-title":"Towards enabling cyber infrastructure as a service in clouds","volume":"39","author":"Wang","year":"2013","journal-title":"Comput. Electr. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kuo, A.M.H. (2011). Opportunities and challenges of cloud computing to improve health care services. J. Med. Internet Res., 13.","DOI":"10.2196\/jmir.1867"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MSP.2009.87","article-title":"Data security in the world of cloud computing","volume":"7","author":"Kaufman","year":"2009","journal-title":"IEEE Secur. Priv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., and Philip, S.Y. (2008). Privacy-preserving data mining: A survey. Handbook of Database Security, Springer.","DOI":"10.1007\/978-0-387-70992-5"},{"key":"ref_5","first-page":"797","article-title":"Approximation algorithms for k-anonymity","volume":"62","author":"Aggarwal","year":"2005","journal-title":"J. Priv. Technol."},{"key":"ref_6","unstructured":"Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., and Zhu, A. (2005, January 5\u20137). Anonymizing tables. Proceedings of the 10th International Conference on Database Theory, Edinburgh, UK."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MWC.2014.6882291","article-title":"A green data transmission mechanism for wireless multimedia sensor networks using information fusion","volume":"21","author":"Zhang","year":"2014","journal-title":"IEEE Wirel. Commun."},{"key":"ref_8","unstructured":"Campan, A, Miller, J., and Sinca, R. (2017, January 25\u201328). A clustering approach for achieving data privacy. Proceedings of the 2007 International Data Mining, Las Vegas, NV, USA."},{"key":"ref_9","unstructured":"Bayardo, R., and Agrawal, R. (2005, January 5\u20138). Data privacy through optimal k-anonymization. Proceedings of the 21st International Conference on Data Engineering (ICDE\u201905), Tokoyo, Japan."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xiao, X., and Tao, Y. (2006, January 27\u201329). Personalized privacy preservation. Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, Chicago, IL, USA.","DOI":"10.1145\/1142473.1142500"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1145\/772862.772867","article-title":"Tools for privacy preserving distributed data mining","volume":"4","author":"Clifton","year":"2002","journal-title":"ACM SIGKDD Explor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10618-005-0007-5","article-title":"Ordinal, continuous and heterogeneous k-anonymity through microaggregation","volume":"11","author":"Torra","year":"2005","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_13","unstructured":"Domingo-Ferrer, J., Solanas, A., and Martinez-Balleste, A. (2006, January 10\u201312). Privacy in statistical databases: K-anonymity through microaggregation. Proceedings of the 2006 IEEE International Conference on Granular Computing, Atlanta, GA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMC.2007.1062","article-title":"Protecting location privacy with personalized k-anonymity: Architecture and algorithms","volume":"7","author":"Gedik","year":"2008","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xiao, X., and Tao, Y. (2007, January 11\u201314). M\u2013Invariance: Towards privacy preserving re-publication of dynamic datasets. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Beijing, China.","DOI":"10.1145\/1247480.1247556"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xia, X., Xiao, Q., and Ji, W. (2012, January 14\u201317). An Efficient Method to Implement Data Private Protection for Dynamic Numerical Sensitive Attributes. Proceedings of the 2012 7th International Conference on Computer Science & Education (ICCSE), Melbourne, VIC, Australia.","DOI":"10.1109\/ICCSE.2012.6295191"},{"key":"ref_17","first-page":"213","article-title":"The invasion of privacy problem and statistics production. An overview","volume":"12","author":"Dalenius","year":"1974","journal-title":"Stat. Tidskr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1142\/S0218488502001648","article-title":"K-anonymity: A model for protecting privacy","volume":"10","author":"Sweeney","year":"2002","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1142\/S021848850200165X","article-title":"Achieving k-anonymity privacy protection using generalization and suppression","volume":"10","author":"Sweeney","year":"2002","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., and Venkatasubramanian, S. (2007, January 15\u201320). T-closeness: Privacy beyond k-anonymity and l-diversity. Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Machanavajjhala, A., Gehrke, J., and Kifer, D. (2006, January 3\u20137). L-diversity: Privacy beyond k-anonymity. Proceedings of the 22nd International Conference on Data Engineering (ICDE\u201906), Atlanta, GA, USA.","DOI":"10.1109\/ICDE.2006.1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/SURV.2014.012214.00007","article-title":"A Survey on Wireless Body Area Networks: Technologies and Design Challenges","volume":"16","author":"Cavallari","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Truta, T., and Vinay, B. (2006, January 3\u20137). Privacy protection: P-sensitive k-anonymity property. Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW\u201906), Atlanta, GA, USA.","DOI":"10.1109\/ICDEW.2006.116"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Domingo-Ferrer, J. (2008, January 4\u20137). A critique of k-anonymity and some of its enhancements. Proceedings of the 2008 Third International Conference on Availability, Reliability and Security, Barcelona, Spain.","DOI":"10.1109\/ARES.2008.97"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jafari, M., Safavi-Naini, R., Saunders, C., and Sheppard, N.P. (2010, January 4). Using digital rights management for securing data in a medical research environment. Proceedings of the Tenth Annual ACM Workshop on Digital Rights Management, Chicago, IL, USA.","DOI":"10.1145\/1866870.1866883"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Anjum, A., and Raschia, G. (2011, January 23\u201324). Privacy-preserving data publiction: A review on \u201cupdates\u201d in continuous data publication. Proceedings of the 2011 International Conference on Information and Communication Technologies, Karachi, Pakistan.","DOI":"10.1109\/ICICT.2011.5983552"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Meyerson, A., and Williams, R. (2004, January 14\u201316). On the complexity of optimal k-anonymity. Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Paris, France.","DOI":"10.1145\/1055558.1055591"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Byun, J., Sohn, Y., Bertino, E., and Li, N. (2006). Secure anonymization for incremental datasets In Workshop on Secure Data Management, Springer.","DOI":"10.1007\/11844662_4"},{"key":"ref_29","unstructured":"Koudas, N., Srivastava, D., Yu, T., and Zhang, Q. (2007, January 15\u201320). Aggregate query answering on anonymized tables. Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey."},{"key":"ref_30","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Demsar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, X., and Tao, Y. (2006, January 12\u201315). Anatomy: Simple and effective privacy preservation. Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea.","DOI":"10.1145\/1142473.1142500"},{"key":"ref_32","unstructured":"Zhang, X., and Bi, H. (2010, January 16\u201318). Secure and effective anonymization against re-publication of dynamic datasets. Proceedings of the 2010 2nd International Conference on Computer Engineering and Technology, Chengdu, China."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, K., and Fung, B. (2006, January 20\u201323). Anonymizing sequential releases. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA.","DOI":"10.1145\/1150402.1150449"},{"key":"ref_34","first-page":"149","article-title":"Efficient anonymizations with enhanced utility","volume":"3","author":"Goldberger","year":"2010","journal-title":"Trans. Data Priv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2834","DOI":"10.1016\/j.ins.2010.04.005","article-title":"Hybrid microdata using microaggregation","volume":"180","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1145\/1866739.1866758","article-title":"A firm foundation for private data analysis","volume":"54","author":"Dwork","year":"2011","journal-title":"Commun. ACM"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, W., Xi, T., Ngai, E.C.H., and Song, Z. (2016). Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing. Sensors, 16.","DOI":"10.3390\/s16060762"},{"key":"ref_38","first-page":"1489","article-title":"Semi-supervised clustering-based anonymous data publishing","volume":"32","author":"Yang","year":"2011","journal-title":"J. Harbin Eng. Univ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"LeFevre, K., DeWitt, D.J., and Ramakrishnan, R. (2005, January 14\u201316). Incognito: Efficient full-domain k-anonymity. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, MD, USA.","DOI":"10.1145\/1066157.1066164"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10618-018-0550-5","article-title":"Kernel mixture model for probability density estimation in Bayesian classifiers","volume":"32","author":"Zhang","year":"2018","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.cose.2012.04.003","article-title":"Semantic adaptive microaggregation of categorical microdata","volume":"31","author":"Valls","year":"2012","journal-title":"Comput. Secur."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, Y., Barman, S., and Naughton, J. (2011, January 11\u201316). Preventing Equivalence Attacks in Updated, anonymized Data. Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, Hannover, Germany.","DOI":"10.1109\/ICDE.2011.5767924"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Spicer, K., and de Wolf, P.-P. (2012). Statistical Disclosure Control, Wiley.","DOI":"10.1002\/9781118348239"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1109\/TCST.2016.2558632","article-title":"Hybrid Sequential Fusion Estimation for Asynchronous Sensor Network-Based Target Trackin","volume":"25","author":"Yang","year":"2017","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.jcss.2014.02.006","article-title":"A security framework in G-Hadoop for big data computing across distributed cloud data centres","volume":"80","author":"Zhao","year":"2014","journal-title":"J. Comput. Syst. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:12:29Z","timestamp":1760195549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,16]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["s18072307"],"URL":"https:\/\/doi.org\/10.3390\/s18072307","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,16]]}}}