{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:43:11Z","timestamp":1767066191968,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":200,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Postdoctoral Science Foundation of Jinan University","award":["XBS1905"],"award-info":[{"award-number":["XBS1905"]}]},{"name":"the Postgraduate education reform project of Jinan University","award":["JDYY1910"],"award-info":[{"award-number":["JDYY1910"]}]},{"name":"University Innovation Team Project of Jinan","award":["2019GXRC015"],"award-info":[{"award-number":["2019GXRC015"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51679058"],"award-info":[{"award-number":["51679058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of <jats:italic>k<\/jats:italic> and <jats:italic>a<\/jats:italic>, i.e. <jats:inline-formula><jats:alternatives><jats:tex-math>$$k$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>k<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u2009=\u2009{3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, <jats:italic>a<\/jats:italic>\u2009=\u2009{0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when <jats:italic>a<\/jats:italic>\u2009=\u20091 and <jats:italic>k<\/jats:italic>\u2009=\u200921, and structure loss approached optimal when <jats:italic>a<\/jats:italic>\u2009=\u20090.4 and <jats:italic>k<\/jats:italic>\u2009=\u20093.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01645-0","type":"journal-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T22:28:11Z","timestamp":1634596091000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Privacy protection of medical data in social network"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4639-6787","authenticated-orcid":false,"given":"Jie","family":"Su","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinming","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"issue":"1","key":"1645_CR1","doi-asserted-by":"publisher","first-page":"S14","DOI":"10.1186\/1755-8794-7-S1-S14","volume":"7","author":"Z Ji","year":"2014","unstructured":"Ji Z, Jiang X, Wang S, Xiong Li, Ohno-Macha L. Differentially private distributed logistic regression using private and public data. BMC Med Genomics. 2014;7(1):S14.","journal-title":"BMC Med Genomics"},{"issue":"1","key":"1645_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.2174\/1574893614666190730110747","volume":"15","author":"W Bao","year":"2020","unstructured":"Bao W, Huang DS, Chen YH. MSIT: Malonylation Sites Identification Tree. Curr Bioinform. 2020;15(1):59\u201367.","journal-title":"Curr Bioinform"},{"key":"1645_CR3","doi-asserted-by":"publisher","first-page":"54073","DOI":"10.1109\/ACCESS.2019.2900275","volume":"7","author":"W Bao","year":"2019","unstructured":"Bao W, Yang B, Huang DS, Wang D, Liu Q, Chen YH, Bao W. IMKPse: identification of protein malonylation sites by the key features into general PseAAC. IEEE Access. 2019;7:54073\u201383.","journal-title":"IEEE Access"},{"key":"1645_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007344","author":"Z Ji","year":"2019","unstructured":"Ji Z, Zhao W, Lin H, Zhou X. Systematically understanding the immunity leading to CRPC progression. PLoS Comput Biol. 2019. https:\/\/doi.org\/10.1371\/journal.pcbi.1007344.","journal-title":"PLoS Comput Biol"},{"key":"1645_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2018.00410","author":"C Liu","year":"2017","unstructured":"Liu C, Chyr J, Zhao W, Xu W, Ji Z, Tan H, Soto C, Zhou X. Genome-wide association and mechanistic studies indicate that immune response contributes to Alzheimer\u2019s disease development. Front Genet. 2017. https:\/\/doi.org\/10.3389\/fgene.2018.00410.","journal-title":"Front Genet"},{"key":"1645_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0080832","author":"H Shao","year":"2014","unstructured":"Shao H, Peng T, Ji Z, Su J, Zhou X. Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects. PLoS ONE. 2014. https:\/\/doi.org\/10.1371\/journal.pone.0080832.","journal-title":"PLoS ONE"},{"issue":"3","key":"1645_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2018.2816018","volume":"2018","author":"M Wang","year":"2018","unstructured":"Wang M, Ji Z, Kim H, Wang S. Selecting optimal subset to release under differentially private M-estimators from hybrid datasets. IEEE Trans Knowl Data Eng. 2018;2018(3):1\u20131.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1645_CR8","doi-asserted-by":"crossref","unstructured":"Suthaharan S. Characterization of differentially private logistic regression. In: The ACMSE 2018 conference. ACM. 2018. p. 1\u20138.","DOI":"10.1145\/3190645.3190682"},{"key":"1645_CR9","first-page":"265","volume":"52","author":"X Meng","year":"2015","unstructured":"Meng X, Zhang X. Big data privacy management. J Comput Res Dev. 2015;52:265\u201381.","journal-title":"J Comput Res Dev"},{"key":"1645_CR10","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/ACCESS.2014.2362522","volume":"2","author":"L Xu","year":"2014","unstructured":"Xu L, Jiang C, Wang J, Yuan J, Ren Y. Information security in big data: privacy and data mining. IEEE Access. 2014;2:1149\u201376.","journal-title":"IEEE Access"},{"key":"1645_CR11","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1109\/ACCESS.2016.2558446","volume":"4","author":"A Mehmood","year":"2016","unstructured":"Mehmood A, Natgunanathan I, Xiang Y, Hua G, Guo S. Protection of big data privacy. IEEE Access. 2016;4:1821\u201334.","journal-title":"IEEE Access"},{"issue":"1","key":"1645_CR12","first-page":"833","volume":"1","author":"G Cormode","year":"2010","unstructured":"Cormode G, Srivastava D, Yu T, Zhang Q. Anonymizing bipartite graph data using safe groupings. VLDB J. 2010;1(1):833\u201344.","journal-title":"VLDB J"},{"key":"1645_CR13","doi-asserted-by":"crossref","unstructured":"Zhang J, Cormode G, Procopiuc CM, Strivastava D, Xiao X. Private release of graph statistics using ladder functions. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data. ACM. 2015. p. 731\u201345.","DOI":"10.1145\/2723372.2737785"},{"issue":"1","key":"1645_CR14","doi-asserted-by":"publisher","first-page":"766","DOI":"10.14778\/1687627.1687714","volume":"2","author":"S Bhagat","year":"2009","unstructured":"Bhagat S, Cormode G, Krishnamurthy B, Strivastava D. Class-based graph anonymization for social network data. Proc VLDB Endow. 2009;2(1):766\u201377.","journal-title":"Proc VLDB Endow"},{"key":"1645_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3125622","volume":"18","author":"B Palanisamy","year":"2018","unstructured":"Palanisamy B, Liu L, Zhou Y, Wang Q. Privacy-preserving publishing of multilevel utility-controlled graph datasets. ACM Trans Internet Technol. 2018;18:1\u201321.","journal-title":"ACM Trans Internet Technol"},{"key":"1645_CR16","doi-asserted-by":"crossref","unstructured":"Campan A, Traian M. A clustering approach for data and structural anonymity in social networks. In: Privacy, security, and trust in KDD Workshop (PinKDD). 2008. p. 33\u201354.","DOI":"10.1007\/978-3-642-01718-6_4"},{"issue":"4","key":"1645_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2700836","volume":"6","author":"H Fu","year":"2015","unstructured":"Fu H, Zhang A, Xie X. Effective social graph deanonymization based on graph structure and descriptive information. ACM Trans Intell Syst Technol. 2015;6(4):1\u201329.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"1645_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9484-8","author":"J Casas-Roma","year":"2017","unstructured":"Casas-Roma J, Herrera-Joancomart\u00ed J, Torra V. A survey of graph-modification techniques for privacy-preserving on networks. Artif Intell Rev. 2017. https:\/\/doi.org\/10.1007\/s10462-016-9484-8.","journal-title":"Artif Intell Rev"},{"issue":"1","key":"1645_CR19","first-page":"153","volume":"17","author":"E Zheleva","year":"2014","unstructured":"Zheleva E, Getoor L. Preserving the privacy of sensitive relationships in graph data. Int J Comput Trends Technol. 2014;17(1):153\u201371.","journal-title":"Int J Comput Trends Technol"},{"key":"1645_CR20","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Li Y, Yu PS. On the hardness of graph anonymization. In: 2011 IEEE 11th international conference on data mining. Vancouver, BC. 2011. p. 1002\u20137.","DOI":"10.1109\/ICDM.2011.112"},{"key":"1645_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05414-4_32","volume-title":"Complex networks and their applications VII. COMPLEX NETWORKS 2018. Studies in computational intelligence","author":"S Horawalavithana","year":"2018","unstructured":"Horawalavithana S, Gandy C, Flores JA, Skvoretz J, Iamnitchi A. Diversity, homophily and the risk of node re-identification in labeled social graphs. In: Aiello L, Cherifi C, Cherifi H, Lambiotte R, Li\u00f3 P, Rocha L, editors. Complex networks and their applications VII. COMPLEX NETWORKS 2018. Studies in computational intelligence, vol. 813. Cham: Springer; 2018. https:\/\/doi.org\/10.1007\/978-3-030-05414-4_32."},{"key":"1645_CR22","doi-asserted-by":"crossref","unstructured":"Karwa V, Slavkovi\u0107 A B, Krivitsky P. Differentially private exponential random graphs. In: Privacy in statistical databases. Springer. 2015. p. 143\u201355.","DOI":"10.1007\/978-3-319-11257-2_12"},{"key":"1645_CR23","doi-asserted-by":"crossref","unstructured":"Sala A, Zhao X, Wilson C, Zheng H and Zhao B Y: Sharing graphs using differentially private graph models. Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. ACM, 2011: 81\u201398.","DOI":"10.1145\/2068816.2068825"},{"key":"1645_CR24","doi-asserted-by":"crossref","unstructured":"Medforth N, Wang K. Privacy risk in graph stream publishing for social network data. In: 2011 IEEE 11th international conference on data mining. IEEE. 2011. p. 437\u201346.","DOI":"10.1109\/ICDM.2011.120"},{"key":"1645_CR25","unstructured":"Rossi L, Musolesi M, Torsello A. On the k-anonymization of time-varying and multi-layer social graphs. In: Proceedings of the international AAAI conference on web and social media. 2015. https:\/\/ojs.aaai.org\/index.php\/ICWSM\/article\/view\/14605."},{"issue":"1","key":"1645_CR26","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10115-010-0311-2","volume":"28","author":"B Zhou","year":"2011","unstructured":"Zhou B, Pei J. The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowl Inf Syst. 2011;28(1):47\u201377.","journal-title":"Knowl Inf Syst"},{"key":"1645_CR27","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-642-01718-6_4","volume":"5456","author":"A Campan","year":"2008","unstructured":"Campan A, Truta TM. Data and structural k-anonymity in social networks. Lect Notes Comput Sci. 2008;5456:33\u201354.","journal-title":"Lect Notes Comput Sci"},{"issue":"4","key":"1645_CR28","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1145\/1749603.1749605","volume":"42","author":"BCM Fung","year":"2010","unstructured":"Fung BCM, Wang K, Chen R, Yu PS. Privacy-preserving data publishing: a survey of recent developments. ACM Comput Surv. 2010;42(4):2623\u20137.","journal-title":"ACM Comput Surv"},{"key":"1645_CR29","unstructured":"Office for Civil Rights. HHS: standards for privacy of individually identifiable health information. Final rule, Fed Regist. 2012. http:\/\/www.hhs.gov\/ocr\/privacy\/hipaa\/administrative\/privacyrule\/adminsimpregtext.pdf."},{"key":"1645_CR30","doi-asserted-by":"crossref","unstructured":"Liu K, Terzi E. Towards identity anonymization on graphs. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM. 2008. p. 93\u2013106.","DOI":"10.1145\/1376616.1376629"},{"key":"1645_CR31","doi-asserted-by":"crossref","unstructured":"Cheng J, Fu AW, Liu J. K-isomorphism: privacy preserving network publication against structural attacks. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, 2010. p. 459\u201370.","DOI":"10.1145\/1807167.1807218"},{"issue":"6","key":"1645_CR32","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s00778-010-0210-x","volume":"19","author":"M Hay","year":"2010","unstructured":"Hay M, Miklau G, Jensen D, Towsley D, Weis P. Resisting structural re-identification in anonymized social networks. VLDB J. 2010;19(6):797\u2013823.","journal-title":"VLDB J"},{"issue":"1","key":"1645_CR33","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1142\/S0218194017500048","volume":"27","author":"P Liu","year":"2017","unstructured":"Liu P, Bai Y, Wang L, Li X. Partial k-anonymity for privacy-preserving social network data publishing. Int J Softw Eng Knowl Eng. 2017;27(1):71\u201390.","journal-title":"Int J Softw Eng Knowl Eng"},{"key":"1645_CR34","doi-asserted-by":"crossref","unstructured":"Byun JW, Kamra A, Bertino E, Li N. Efficient k-anonymization using clustering techniques. In: International conference on database systems for advanced applications. Berlin: Springer. 2007. p. 188\u201320.","DOI":"10.1007\/978-3-540-71703-4_18"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01645-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-021-01645-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01645-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T22:31:27Z","timestamp":1634596287000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-021-01645-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4]]},"references-count":34,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1645"],"URL":"https:\/\/doi.org\/10.1186\/s12911-021-01645-0","relation":{},"ISSN":["1472-6947"],"issn-type":[{"type":"electronic","value":"1472-6947"}],"subject":[],"published":{"date-parts":[[2021,4]]},"assertion":[{"value":"12 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"286"}}