{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T11:13:13Z","timestamp":1774005193217,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T00:00:00Z","timestamp":1526515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The topic of big data has attracted increasing interest in recent years. The emergence of big data leads to new difficulties in terms of protection models used for data privacy, which is of necessity for sharing and processing data. Protecting individuals\u2019 sensitive information while maintaining the usability of the data set published is the most important challenge in privacy preserving. In this regard, data anonymization methods are utilized in order to protect data against identity disclosure and linking attacks. In this study, a novel data anonymization algorithm based on chaos and perturbation has been proposed for privacy and utility preserving in big data. The performance of the proposed algorithm is evaluated in terms of Kullback\u2013Leibler divergence, probabilistic anonymity, classification accuracy, F-measure and execution time. The experimental results have shown that the proposed algorithm is efficient and performs better in terms of Kullback\u2013Leibler divergence, classification accuracy and F-measure compared to most of the existing algorithms using the same data set. Resulting from applying chaos to perturb data, such successful algorithm is promising to be used in privacy preserving data mining and data publishing.<\/jats:p>","DOI":"10.3390\/e20050373","type":"journal-article","created":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T11:47:45Z","timestamp":1526557665000},"page":"373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6133-8617","authenticated-orcid":false,"given":"Can","family":"Eyupoglu","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Istanbul Commerce University, Istanbul 34840, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammed Ali","family":"Aydin","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Istanbul University, Istanbul 34320, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul Halim","family":"Zaim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Istanbul Commerce University, Istanbul 34840, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet","family":"Sertbas","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Istanbul University, Istanbul 34320, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Big Data: Survey, Technologies, Opportunities, and Challenges","volume":"2014","author":"Khan","year":"2014","journal-title":"Sci. World J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/CC.2014.7085614","article-title":"Big Data security and privacy: A review","volume":"11","author":"Matturdi","year":"2014","journal-title":"China Commun."},{"key":"ref_3","unstructured":"Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A.H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity."},{"key":"ref_4","first-page":"10","article-title":"Data, data, everywhere","volume":"87","author":"McCune","year":"1998","journal-title":"Manag. Rev."},{"key":"ref_5","first-page":"5","article-title":"Big data security","volume":"2012","author":"Tankard","year":"2012","journal-title":"Netw. Secur."},{"key":"ref_6","unstructured":"Gantz, J., and Reinsel, D. (2013). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East\u2014United States. IDC Country Brief, IDC Analyze the Future, IDC."},{"key":"ref_7","unstructured":"Bamford, J. (2018, April 21). The NSA Is Building the Country\u2019s Biggest Spy Center (Watch What You Say). Wired. Available online: https:\/\/www.wired.com\/2012\/03\/ff_nsadatacenter\/all\/1\/."},{"key":"ref_8","unstructured":"Ardagna, C.A., and Damiani, E. (2014, January 16\u201317). Business Intelligence meets Big Data: An Overview on Security and Privacy. Proceedings of the NSF Workshop on Big Data Security and Privacy, Dallas, TX, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.14778\/2367502.2367572","article-title":"Challenges and Opportunities with Big Data","volume":"5","author":"Labrinidis","year":"2012","journal-title":"Proc. VLDB Endow."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/S1353-4858(15)70009-7","article-title":"The big data security challenge","volume":"2015","author":"Lafuente","year":"2015","journal-title":"Netw. Secur."},{"key":"ref_11","first-page":"177","article-title":"Preserving Individual Privacy in Big Data","volume":"10","author":"Zaim","year":"2017","journal-title":"Int. J. Inf. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2016.08.011","article-title":"Research issues for privacy and security of electronic health services","volume":"68","author":"Yuksel","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.comnet.2014.11.008","article-title":"Security, privacy and trust in Internet of Things: The road ahead","volume":"76","author":"Sicari","year":"2015","journal-title":"Comput. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.future.2014.10.010","article-title":"A lightweight attribute-based encryption scheme for the Internet of Things","volume":"49","author":"Yao","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.future.2015.09.016","article-title":"A comprehensive approach to privacy in the cloud-based Internet of things","volume":"56","author":"Henze","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.future.2016.10.022","article-title":"Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop","volume":"74","author":"Nayahi","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., and Yu, P.S. (2008). Privacy-Preserving Data Mining: Models and Algorithms, Springer.","DOI":"10.1007\/978-0-387-70992-5"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1749603.1749605","article-title":"Privacy preserving data publishing: A survey on recent developments","volume":"42","author":"Fung","year":"2010","journal-title":"ACM Comput. Surv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.future.2014.03.002","article-title":"PPFSCADA: Privacy preserving framework for SCADA data publishing","volume":"37","author":"Fahad","year":"2014","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/ACCESS.2014.2362522","article-title":"Information Security in Big Data: Privacy and Data Mining","volume":"2","author":"Xu","year":"2014","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1142\/S0218488515500300","article-title":"An Efficient Clustering for Anonymizing Data and Protecting Sensitive Labels","volume":"23","author":"Nayahi","year":"2015","journal-title":"Int. J. Uncertain. Fuzz."},{"key":"ref_22","first-page":"51","article-title":"Guaranteeing anonymity when sharing medical data, the Datafly system","volume":"1997","author":"Sweeney","year":"1997","journal-title":"Proc. AMIA Annu Fall Symp."},{"key":"ref_23","unstructured":"Sweeney, L. (1997, January 10\u201313). Datafly: A system for providing anonymity in medical data. Proceedings of the Eleventh International Conference on Database Security, Lake Tahoe, CA, USA."},{"key":"ref_24","unstructured":"Samarati, P., and Sweeney, L. (1998, January 3\u20136). Protecting privacy when disclosing information: K-anonymity and its enforcement through generalization and suppression. Proceedings of the IEEE Symposium on Research in Security and Privacy, Oakland, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/69.971193","article-title":"Protecting respondents\u2019 identities in microdata release","volume":"13","author":"Samarati","year":"2001","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_26","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. Fuzz."},{"key":"ref_27","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. Fuzz."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s11227-013-0926-7","article-title":"Reversible privacy preserving data mining: A combination of difference expansion and privacy preserving","volume":"66","author":"Chen","year":"2013","journal-title":"J. Supercomput."},{"key":"ref_29","unstructured":"Domingo-Ferrer, J., Mateo-Sanz, J.M., and Torra, V. (2001, January 7\u201310). Comparing SDC methods for microdata on the basis of information loss and disclosure risk. Proceedings of the International Conference on New Techniques and Technologies for Statistics: Exchange of Technology and Knowhow, New York, NY, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1016\/j.cose.2010.05.005","article-title":"Classifying data from protected statistical datasets","volume":"29","author":"Herranz","year":"2010","journal-title":"Comput. Secur."},{"key":"ref_31","unstructured":"Kim, J.J., and Winkler, W.E. (2003). Multiplicative Noise for Masking Continuous Data, Statistical Research Division."},{"key":"ref_32","first-page":"92","article-title":"Random projection-based multiplicative data perturbation for privacy preserving distributed data mining","volume":"18","author":"Liu","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.eswa.2009.05.097","article-title":"A novel anonymization algorithm: Privacy protection and knowledge preservation","volume":"37","author":"Yang","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.dss.2009.07.003","article-title":"Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining","volume":"48","author":"Zhu","year":"2009","journal-title":"Decis. Support Syst."},{"key":"ref_35","unstructured":"Chen, K., Sun, G., and Liu, L. Towards attack-resilient geometric data perturbation. Proceedings of the Seventh SIAM International Conference on Data Mining."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.1109\/TPDS.2009.26","article-title":"Privacy-preserving multiparty collaborative mining with geometric data perturbation","volume":"20","author":"Chen","year":"2009","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10115-010-0362-4","article-title":"Geometric data perturbation for privacy preserving outsourced data mining","volume":"29","author":"Chen","year":"2011","journal-title":"Knowl. Inf. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.knosys.2011.05.011","article-title":"Privacy preserving data mining: A noise addition framework using a novel clustering technique","volume":"24","author":"Islam","year":"2011","journal-title":"Knowl. Based Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/772862.772865","article-title":"Cryptographic techniques for privacy-preserving data mining","volume":"4","author":"Pinkas","year":"2002","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.future.2014.10.014","article-title":"Secure sharing of personal health records in cloud computing: Ciphertext-policy attribute-based signcryption","volume":"52","author":"Liu","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_41","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 ACM SIGMOD International Conference on Management of Data, Baltimore, MD, USA.","DOI":"10.1145\/1066157.1066164"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"LeFevre, K., DeWitt, D.J., and Ramakrishnan, R. (2006, January 3\u20137). Mondrian multidimensional k-anonymity. Proceedings of the 22nd International Conference on Data Engineering, Atlanta, GA, USA.","DOI":"10.1109\/ICDE.2006.101"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1217299.1217302","article-title":"L-diversity: Privacy beyond k-anonymity","volume":"1","author":"Machanavajjhala","year":"2007","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_44","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 IEEE International Conference on Data Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.future.2010.07.007","article-title":"A family of enhanced (L, \u03b1) diversity models for privacy preserving data publishing","volume":"27","author":"Sun","year":"2011","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Agrawal, R., and Srikant, R. (2000, January 16\u201318). Privacy preserving data mining. Proceedings of the ACM SIGMOD Conference on Management of Data, Dallas, TX, USA.","DOI":"10.1145\/342009.335438"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Agrawal, D., and Aggarwal, C.C. (2001, January 21\u201324). On the design and quantification of privacy preserving data mining algorithms. Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Santa Barbara, CA, USA.","DOI":"10.1145\/375551.375602"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Evfimievski, A., Srikant, R., Agrawal, R., and Gehrke, J. (2002, January 23\u201325). Privacy preserving mining of association rules. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD\u201902), Edmonton, AB, Canada.","DOI":"10.1145\/775079.775080"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Evfimevski, A., Gehrke, J., and Srikant, R. (2003, January 9\u201312). Limiting privacy breaches in privacy preserving data mining. Proceedings of the ACM SIGMOD\/PODS Conference, San Diego, CA, USA.","DOI":"10.1145\/773153.773174"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rizvi, S.J., and Haritsa, J.R. (2002, January 20\u201323). Maintaining data privacy in association rule mining. Proceedings of the 28th VLDB Conference, Hong Kong, China.","DOI":"10.1016\/B978-155860869-6\/50066-4"},{"key":"ref_51","unstructured":"Dwork, C. (2006, January 9\u201316). Differential privacy. Proceedings of the 33rd International Conference on Automata, Languages and Programming, Venice, Italy."},{"key":"ref_52","unstructured":"Zhang, X., Qi, L., Dou, W., He, Q., Leckie, C., Ramamohanarao, K., and Salcic, Z. (2017). MRMondrian: Scalable Multidimensional Anonymisation for Big Data Privacy Preservation. IEEE Trans. Big Data."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, Z., Miklau, G., Winslett, M., and Xiao, X. (2012, January 20\u201324). Differential privacy in data publication and analysis. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, Scottsdale, AZ, USA.","DOI":"10.1145\/2213836.2213910"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.tcs.2016.01.015","article-title":"Preserving differential privacy under finite-precision semantics","volume":"655","author":"Gazeau","year":"2016","journal-title":"Theor. Comput. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006, January 4\u20137). Calibrating noise to sensitivity in private data analysis. Proceedings of the Theory of Cryptography Conference (TCC), New York, NY, USA.","DOI":"10.1007\/11681878_14"},{"key":"ref_56","first-page":"1","article-title":"Achieving differential privacy of trajectory data publishing in participatory sensing","volume":"400\u2013401","author":"Li","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"McSherry, F., and Talwar, K. (2007, January 20\u201323). Mechanism design via differential privacy. Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, Providence, RI, USA.","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mohammed, N., Chen, R., Fung, B., and Yu, P.S. (2011, January 21\u201324). Differentially private data release for data mining. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA.","DOI":"10.1145\/2020408.2020487"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chen, R., Fung, B.C.M., and Desai, B.C. (arXiv, 2011). Differentially Private trajectory Data Publication, arXiv.","DOI":"10.1145\/2339530.2339564"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, N., Qardaji, W., and Su, D. (2012, January 2\u20134). On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy. Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security, Seoul, Korea.","DOI":"10.1145\/2414456.2414474"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s00778-014-0351-4","article-title":"Enhancing data utility in differential privacy via microaggregation-based k-anonymity","volume":"23","year":"2014","journal-title":"VLDB J."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1109\/TKDE.2013.107","article-title":"A supermodularity-based differential privacy preserving algorithm for data anonymization","volume":"26","author":"Fouad","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_63","unstructured":"Wang, X., and Jin, Z. (2016, January 14\u201317). A differential privacy multidimensional data release model. Proceedings of the 2nd IEEE International Conference on Computer and Communications, Chengdu, China."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Xiong, L., and Yuan, C. (2010). Differentially private data release through multidimensional partitioning. Workshop on Secure Data Management, Springer.","DOI":"10.1007\/978-3-642-15546-8_11"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zaman, A.N.K., Obimbo, C., and Dara, R.A. (2017, January 21\u201322). An improved differential privacy algorithm to protect re-identification of data. Proceedings of the 2017 IEEE Canada International Humanitarian Technology Conference, Toronto, ON, Canada.","DOI":"10.1109\/IHTC.2017.8058174"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Koufogiannis, F., and Pappas, G.J. (2017, January 12\u201315). Differential privacy for dynamical sensitive data. Proceedings of the IEEE 56th Annual Conference on Decision and Control, Melbourne, Australia.","DOI":"10.1109\/CDC.2017.8263806"},{"key":"ref_67","unstructured":"Li, L.-X., Ding, Y.-S., and Wang, J.-Y. (2017, January 12\u201314). Differential Privacy Data Protection Method Based on Clustering. Proceedings of the 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Nanjing, China."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Dong, B., Liu, R., and Wang, W.H. (2014, January 3\u20137). PraDa: Privacy-preserving Data-Deduplication-as-a-Service. Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, Shanghai, China.","DOI":"10.1145\/2661829.2661863"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.compeleceng.2015.11.008","article-title":"A chaos-based image encryption algorithm with simple logical functions","volume":"54","author":"Yavuz","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_70","unstructured":"Kohavi, R., and Becker, B. (2018, April 21). Adult Data Set, Data Mining and Visualization Silicon Graphics. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/adult."},{"key":"ref_71","unstructured":"Lichman, M. (2013). UCI Machine Learning Repository, University of California, School of Information and Computer Science. Available online: https:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On Information and Sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_73","unstructured":"Han, J., Kamber, M., and Pei, J. (2012). Data Mining Concepts and Techniques, Elsevier, Morgan Kaufmann Publishers. [3rd ed.]."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/5\/373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:41Z","timestamp":1760195081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/5\/373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,17]]},"references-count":73,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["e20050373"],"URL":"https:\/\/doi.org\/10.3390\/e20050373","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,17]]}}}