{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:49:43Z","timestamp":1740116983092,"version":"3.37.3"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1433116"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["NP2017208"],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of Graduate Innovation Center in NUAA","award":["Kfjj20191603"]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1016\/j.eswa.2020.113241","type":"journal-article","created":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:44:13Z","timestamp":1580517853000},"page":"113241","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":33,"special_numbering":"C","title":["Novel trajectory privacy-preserving method based on clustering using differential privacy"],"prefix":"10.1016","volume":"149","author":[{"given":"Xiaodong","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Dechang","family":"Pi","sequence":"additional","affiliation":[]},{"given":"Junfu","family":"Chen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2020.113241_bib0001","series-title":"Proceedings of the 2013 ACM SIGSAC conference on computer & communications security (CCS '13)","first-page":"901","article-title":"Geo-indistinguishability: Differential privacy for location-based systems","author":"Andr\u00e9s","year":"2013"},{"key":"10.1016\/j.eswa.2020.113241_bib0002","series-title":"Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems","first-page":"128","article-title":"Practical privacy: Thesulq framework[C]","author":"Blum","year":"2005"},{"key":"10.1016\/j.eswa.2020.113241_bib0004","series-title":"2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","first-page":"1","article-title":"Trajectory anonymization: Balancing usefulness about position information and timestamp[c]","author":"Chiba","year":"2019"},{"key":"10.1016\/j.eswa.2020.113241_bib0005","series-title":"2019 4th International Conference on Computing, Communications and Security (ICCCS)","first-page":"1","article-title":"Clustering geo-indistinguishability for privacy of continuous location traces[c]","volume":"2019","author":"Cunha","year":"2019"},{"key":"10.1016\/j.eswa.2020.113241_bib0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2018.06.005","article-title":"PLDP-TD: Personalized-location differentially private data analysis on trajectory databases","volume":"49","author":"Deldar","year":"2018","journal-title":"Pervasive and Mobile Computing"},{"key":"10.1016\/j.eswa.2020.113241_bib0007","series-title":"Proceedings of the 33rd international colloquium on automata","first-page":"1","article-title":"Differential privacy[C]","author":"Dwork","year":"2006"},{"key":"10.1016\/j.eswa.2020.113241_bib0008","unstructured":"Dwork, C., Naor, M., Pitassi, T., Rothblum, G. N., & Yekhanin, S. (2010). Pan-Private streaming algorithms[c]\/\/ics. 2010: 66\u201380."},{"key":"10.1016\/j.eswa.2020.113241_bib0009","unstructured":"Fisher, B. (2010). Edinburgh informatics forum pedestrian database [OL]. http:\/\/homepages.i-nf.ed.ac.uk\/rbf\/FORUMTRACKING."},{"key":"10.1016\/j.eswa.2020.113241_bib0010","series-title":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","first-page":"265","article-title":"Composition attacks and auxiliary information in data privacy[C]","author":"Ganta","year":"2008"},{"issue":"1","key":"10.1016\/j.eswa.2020.113241_bib0011","article-title":"Trajectory data privacy protection based on differential privacy mechanism[C]\/\/IOP conference series: Materials science and engineering","volume":"351","author":"Gu","year":"2018","journal-title":"IOP Publishing"},{"issue":"141","key":"10.1016\/j.eswa.2020.113241_bib0012","article-title":"Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks","volume":"2020","author":"Langari","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2020.113241_bib0013","doi-asserted-by":"crossref","unstructured":"Li, N., Qardaji, W., & Su, D. (2010). Provably private data anonymization: Or, k-Anonymity meets differential privacy. Corr, 32\u201333. doi:10.1145\/2414456.2414474.","DOI":"10.1145\/2414456.2414474"},{"issue":"5","key":"10.1016\/j.eswa.2020.113241_bib0014","first-page":"134","article-title":"Spatio-temporal aware privacy-preserving scheme in LBS","volume":"39","author":"Li","year":"2018","journal-title":"Journal on Communications"},{"key":"10.1016\/j.eswa.2020.113241_bib0015","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/2518714","article-title":"Differentially private release of the distribution of clustering coefficients across communities","volume":"2019","author":"Li","year":"2019","journal-title":"Security and Communication Networks"},{"key":"10.1016\/j.eswa.2020.113241_bib0016","doi-asserted-by":"crossref","unstructured":"Liu, X., Guo, Y., Chen, Y., & Tan, X. (2018). Trajectory privacy protection on spatial streaming data with differential privacy[c]\/\/2018 ieee global communications conference (GLOBECOM). IEEE, 1\u20137. doi:10.1109\/GLOCOM.2018.8647918.","DOI":"10.1109\/GLOCOM.2018.8647918"},{"issue":"5","key":"10.1016\/j.eswa.2020.113241_bib0017","first-page":"125","article-title":"Differentially private data release based on clustering anonymization","volume":"37","author":"Liu","year":"2016","journal-title":"Journal of Communication"},{"key":"10.1016\/j.eswa.2020.113241_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112858","article-title":"A personal data store approach for recommender systems: Enhancing privacy without sacrificing accuracy","volume":"139","author":"Mazeh","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2020.113241_bib0019","series-title":"Proceedings of the 48th annual ieee symposium on Foundations of Computer Science (FOCS\u201907)","first-page":"94","article-title":"Mechanism design via differential privacy[C].","author":"McSherry","year":"2007"},{"key":"10.1016\/j.eswa.2020.113241_bib0020","doi-asserted-by":"crossref","first-page":"21053","DOI":"10.1109\/ACCESS.2018.2824798","article-title":"DP-MCDBSCAN: Differential privacy preserving multi-core dbscan clustering for network user data[j]","volume":"6","author":"Ni","year":"2018","journal-title":"IEEE Access : Practical Innovations, Open solutions"},{"year":"2018","series-title":"Releasing correlated trajectories: Towards high utility and optimal differential privacy","author":"Ou","key":"10.1016\/j.eswa.2020.113241_bib0022"},{"key":"10.1016\/j.eswa.2020.113241_bib0023","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.future.2018.10.025","article-title":"Multidimensional privacy preservation in location-based services","volume":"93","author":"Peng","year":"2019","journal-title":"Future Generation Computer Systems"},{"issue":"71","key":"10.1016\/j.eswa.2020.113241_bib0024","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.eswa.2016.11.018","article-title":"Privacy-preserving collaborative recommendations based on random perturbations","volume":"2017","author":"Polatidis","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2020.113241_bib0025","series-title":"2017 IEEE Second International Conference on Data Science in Cyberspace (DSC)","first-page":"133","article-title":"DPLK-means: A novel differential privacy K-means mechanism[C]","author":"Ren","year":"2017"},{"key":"10.1016\/j.eswa.2020.113241_bib0026","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.ins.2014.11.005","article-title":"Privacy by diversity in sequential releases of databases","volume":"298","author":"Shmueli","year":"2015","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2020.113241_bib0027","series-title":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy (CODASPY '16)","first-page":"26","article-title":"Differentially private K-Means clustering","author":"Su","year":"2016"},{"key":"10.1016\/j.eswa.2020.113241_bib0028","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.jpdc.2018.09.005","article-title":"A privacy preserving location service for cloud-of-things system","volume":"123","author":"Tian","year":"2019","journal-title":"Journal of Parallel and Distributed Computing"},{"issue":"4","key":"10.1016\/j.eswa.2020.113241_bib0029","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.is.2012.12.003","article-title":"On the privacy offered by (k, \u03b4)-anonymity","volume":"38","author":"Trujillo","year":"2013","journal-title":"Information Systems"},{"issue":"1","key":"10.1016\/j.eswa.2020.113241_bib0030","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TNSM.2018.2877790","article-title":"Protecting trajectory from semantic attack considering k -Anonymity, l -Diversity, and t-Closeness","volume":"16","author":"Tu","year":"2018","journal-title":"IEEE Transactions on Network and Service Management"},{"issue":"6","key":"10.1016\/j.eswa.2020.113241_bib0031","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.3233\/IDA-163098","article-title":"Cluster-indistinguishability: A practical differential privacy mechanism for trajectory clustering","volume":"21","author":"Wang","year":"2017","journal-title":"Intelligent Data Analysis"},{"key":"10.1016\/j.eswa.2020.113241_bib0032","series-title":"Advances in Neural Information Processing Systems(NIPS)","first-page":"1000","article-title":"Differentially private subspace clustering[C]","author":"Wang","year":"2015"},{"key":"10.1016\/j.eswa.2020.113241_bib0033","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jpdc.2019.07.002","article-title":"Differential privacy-based trajectory community recommendation in social network","volume":"133","author":"Wei","year":"2019","journal-title":"Journal of Parallel and Distributed Computing"},{"issue":"3","key":"10.1016\/j.eswa.2020.113241_bib0034","first-page":"578","article-title":"A clustering hybrid based algorithm for privacy preserving trajectory data publishing","volume":"50","author":"Yingjie","year":"2013","journal-title":"Journal of Computer Research and Development"},{"key":"10.1016\/j.eswa.2020.113241_bib0035","series-title":"Proceedings of the 22nd ACM SIGSAC conference on computer and communications security","first-page":"1298","article-title":"Protecting locations with differential privacy under temporal correlations[C]","author":"Xiao","year":"2015"},{"key":"10.1016\/j.eswa.2020.113241_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112846","article-title":"TAD: A trajectory clustering algorithm based on spatial-temporal density analysis","volume":"139","author":"Yang","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"10.1016\/j.eswa.2020.113241_bib0037","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/j.eswa.2014.08.037","article-title":"A fast perturbation algorithm using tree structure for privacy preserving utility mining","volume":"42","author":"Yun","year":"2015","journal-title":"Expert Systems with Applications"},{"issue":"49","key":"10.1016\/j.eswa.2020.113241_bib0038","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.asoc.2016.09.003","article-title":"Monitoring vehicle outliers based on clustering technique","volume":"2016","author":"Yun","year":"2016","journal-title":"Applied Soft Computing"},{"issue":"9","key":"10.1016\/j.eswa.2020.113241_bib0039","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.3390\/s19092190","article-title":"A trajectory privacy preserving scheme in the CANNQ service for IoT","volume":"19","author":"Zhang","year":"2019","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.eswa.2020.113241_bib0040","first-page":"927","article-title":"Differential privacy in data publication and analysis","volume":"37","author":"Zhang","year":"2014","journal-title":"Chinese Journal of Computers"},{"key":"10.1016\/j.eswa.2020.113241_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.07.008","article-title":"Novel trajectory data publishing method under differential privacy","volume":"138","author":"Zhao","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2020.113241_bib0042","series-title":"2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS)","first-page":"1","article-title":"Trajectory protection scheme based on fog computing and K-anonymity in IoT[c]","author":"Zhou","year":"2019"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417420300671?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417420300671?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T22:18:32Z","timestamp":1586643512000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417420300671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":40,"alternative-id":["S0957417420300671"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2020.113241","relation":{},"ISSN":["0957-4174"],"issn-type":[{"type":"print","value":"0957-4174"}],"subject":[],"published":{"date-parts":[[2020,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Novel trajectory privacy-preserving method based on clustering using differential privacy","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2020.113241","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"113241"}}