{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:15:39Z","timestamp":1778148939039,"version":"3.51.4"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07393-0","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T03:33:01Z","timestamp":1654227181000},"page":"13355-13369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8059-7094","authenticated-orcid":false,"given":"H\u00e9ber Hwang","family":"Arcolezi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6437-5598","authenticated-orcid":false,"given":"Jean-Fran\u00e7ois","family":"Couchot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0381-4862","authenticated-orcid":false,"given":"Denis","family":"Renaud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-9905","authenticated-orcid":false,"given":"Bechara","family":"Al Bouna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0914-4580","authenticated-orcid":false,"given":"Xiaokui","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"issue":"1","key":"7393_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.286","volume":"5","author":"YA de Montjoye","year":"2018","unstructured":"de Montjoye YA, Gambs S, Blondel V, Canright G, de Cordes N, Deletaille S, Eng\u00f8-Monsen K, Garcia-Herranz M, Kendall J, Kerry C, Krings G, Letouz\u00e9 E, Luengo-Oroz M, Oliver N, Rocher L, Rutherford A, Smoreda Z, Steele J, Wetter E, Pentland A, Bengtsson L (2018) On the privacy-conscientious use of mobile phone data. Sci Data 5(1):1\u20136. https:\/\/doi.org\/10.1038\/sdata.2018.286","journal-title":"Sci Data"},{"issue":"6487","key":"7393_CR2","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1126\/science.abb8021","volume":"368","author":"CO Buckee","year":"2020","unstructured":"Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, Grad YH, Grenfell B, Halloran ME, Kraemer MUG, Lipsitch M, Metcalf CJE, Meyers LA, Perkins TA, Santillana M, Scarpino SV, Viboud C, Wesolowski A, Schroeder A (2020) Aggregated mobility data could help fight COVID-19. Science 368(6487):145\u2013146. https:\/\/doi.org\/10.1126\/science.abb8021","journal-title":"Science"},{"issue":"1","key":"7393_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1140\/epjds\/s13688-015-0046-0","volume":"4","author":"VD Blondel","year":"2015","unstructured":"Blondel VD, Decuyper A, Krings G (2015) A survey of results on mobile phone datasets analysis. EPJ Data Sci 4(1):10. https:\/\/doi.org\/10.1140\/epjds\/s13688-015-0046-0","journal-title":"EPJ Data Sci"},{"issue":"23","key":"7393_CR4","doi-asserted-by":"publisher","first-page":"eabc0764","DOI":"10.1126\/sciadv.abc0764","volume":"6","author":"N Oliver","year":"2020","unstructured":"Oliver N, Lepri B, Sterly H, Lambiotte R, Deletaille S, Nadai MD, Letouz\u00e9 E, Salah AA, Benjamins R, Cattuto C, Colizza V, de Cordes N, Fraiberger SP, Koebe T, Lehmann S, Murillo J, Pentland A, Pham PN, Pivetta F, Saram\u00e4ki J, Scarpino SV, Tizzoni M, Verhulst S, Vinck P (2020) Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Sci Adv 6(23):eabc0764. https:\/\/doi.org\/10.1126\/sciadv.abc0764","journal-title":"Sci Adv"},{"issue":"1","key":"7393_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3485125","volume":"55","author":"M Luca","year":"2021","unstructured":"Luca M, Barlacchi G, Lepri B, Pappalardo L (2021) A survey on deep learning for human mobility. ACM Comput Surv 55(1):1\u201344. https:\/\/doi.org\/10.1145\/3485125","journal-title":"ACM Comput Surv"},{"key":"7393_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/dap.2021.6","author":"PA de Alarcon","year":"2021","unstructured":"de Alarcon PA, Salevsky A, Gheti-Kao D, Rosalen W, Duarte MC, Cuervo C, Mu\u00f1oz JJ, Pascual JM, Schurig M, Tre\u00df T, Diaz E, de la Cuesta C, Frias-Martinez E (2021) The contribution of telco data to fight the COVID-19 pandemic: experience of telefonica throughout its footprint. Data Policy. https:\/\/doi.org\/10.1017\/dap.2021.6","journal-title":"Data Policy"},{"issue":"4","key":"7393_CR7","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.3390\/su12041501","volume":"12","author":"S Dujardin","year":"2020","unstructured":"Dujardin S, Jacques D, Steele J, Linard C (2020) Mobile phone data for urban climate change adaptation: reviewing applications, opportunities and key challenges. Sustainability 12(4):1501. https:\/\/doi.org\/10.3390\/su12041501","journal-title":"Sustainability"},{"key":"7393_CR8","doi-asserted-by":"publisher","unstructured":"Hong L, Lee M, Mashhadi A, Frias-Martinez V (2018) Towards understanding communication behavior changes during floods using cell phone data. In: Lecture Notes in Computer Science, pp 97\u2013107. Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-01159-8_9","DOI":"10.1007\/978-3-030-01159-8_9"},{"key":"7393_CR9","unstructured":"World-Health-Organization: WHO announces COVID-19 outbreak a pandemic. Available online: https:\/\/www.euro.who.int\/en\/health-topics\/health-emergencies\/coronavirus-covid-19\/news\/news\/2020\/3\/who-announces-covid-19-outbreak-a-pandemic (Accessed on 07 September 2020)"},{"key":"7393_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/dap.2021.9","author":"M Vespe","year":"2021","unstructured":"Vespe M, Iacus SM, Santamaria C, Sermi F, Spyratos S (2021) On the use of data from multiple mobile network operators in europe to fight COVID-19. Data Policy. https:\/\/doi.org\/10.1017\/dap.2021.9","journal-title":"Data Policy"},{"key":"7393_CR11","unstructured":"European-Commission: Commission recommendation (eu) 2020\/518 of 8 April 2020 on a common union toolbox for the use of technology and data to combat and exit from the COVID-19 crisis, in particular concerning mobile applications and the use of anonymised mobility data. Available online: https:\/\/eur-lex.europa.eu\/eli\/reco\/2020\/518\/oj (Accessed on 04 July 2021)"},{"key":"7393_CR12","unstructured":"Confinements li\u00e9s \u00e0 la pand\u00e9mie de COVID-19 en france. Available online: https:\/\/fr.wikipedia.org\/wiki\/Confinements_li%C3%A9s_%C3%A0_la_pand%C3%A9mie_de_Covid-19_en_France (Accessed on 11 July 2021)"},{"issue":"1","key":"7393_CR13","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1038\/srep01376","volume":"3","author":"YA de Montjoye","year":"2013","unstructured":"de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD (2013) Unique in the crowd: the privacy bounds of human mobility. Sci Rep 3(1):1376. https:\/\/doi.org\/10.1038\/srep01376","journal-title":"Sci Rep"},{"issue":"3","key":"7393_CR14","first-page":"79","volume":"14","author":"T Murakami","year":"2021","unstructured":"Murakami T, Takahashi K (2021) Toward evaluating re-identification risks in the local privacy model. Trans Data Privacy 14(3):79\u2013116","journal-title":"Trans Data Privacy"},{"key":"7393_CR15","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/978-3-030-72465-8_3","volume-title":"Privacy and identity management 2020","author":"HH Arcolezi","year":"2021","unstructured":"Arcolezi HH, Couchot JF, Bouna BA, Xiao X (2021) Longitudinal collection and analysis of mobile phone data with local differential privacy. In: Friedewald M, Schiffner S, Krenn S (eds) Privacy and identity management. Springer International Publishing, Cham, pp 40\u201357.\u00a0https:\/\/doi.org\/10.1007\/978-3-030-72465-8_3","edition":"619"},{"key":"7393_CR16","doi-asserted-by":"publisher","unstructured":"Alaggan M, Gambs S, Matwin S, Tuhin M (2015) Sanitization of call detail records via differentially-private bloom filters. In: Data and applications security and privacy XXIX,. Springer International Publishing, Cham, pp 223\u2013230. https:\/\/doi.org\/10.1007\/978-3-319-20810-7_15","DOI":"10.1007\/978-3-319-20810-7_15"},{"key":"7393_CR17","doi-asserted-by":"publisher","unstructured":"Acs G, Castelluccia C (2014) A case study: privacy preserving release of spatio-temporal density in Paris. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD \u201914. ACM Press. https:\/\/doi.org\/10.1145\/2623330.2623361","DOI":"10.1145\/2623330.2623361"},{"key":"7393_CR18","unstructured":"General data protection regulation (GDPR) (2018) Available online: https:\/\/gdpr-info.eu\/ (Accessed on 04 July 2021)"},{"key":"7393_CR19","unstructured":"Commission nationale de l\u2019informatique et des libert\u00e9s (CNIL) (1978) Available online: https:\/\/www.cnil.fr\/en\/home (Accessed on 04 July 2021)"},{"key":"7393_CR20","doi-asserted-by":"publisher","unstructured":"Xu F, Tu Z, Li Y, Zhang P, Fu X, Jin D (2017) Trajectory recovery from ASH. In: Proceedings of the 26th International Conference on World Wide Web, pp 1241\u20131250. International World Wide Web Conferences Steering Committee. https:\/\/doi.org\/10.1145\/3038912.3052620","DOI":"10.1145\/3038912.3052620"},{"issue":"3","key":"7393_CR21","doi-asserted-by":"publisher","first-page":"1446","DOI":"10.1109\/tnet.2018.2829173","volume":"26","author":"Z Tu","year":"2018","unstructured":"Tu Z, Xu F, Li Y, Zhang P, Jin D (2018) A new privacy breach: user trajectory recovery from aggregated mobility data. IEEE\/ACM Trans Netw 26(3):1446\u20131459. https:\/\/doi.org\/10.1109\/tnet.2018.2829173","journal-title":"IEEE\/ACM Trans Netw"},{"key":"7393_CR22","unstructured":"Orange-Business-Services: Flux vision: real time statistics on mobility patterns (2013) Available online: https:\/\/www.orange-business.com\/en\/products\/flux-vision (accessed on 01 July 2021)"},{"issue":"4","key":"7393_CR23","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1515\/popets-2017-0043","volume":"2017","author":"A Pyrgelis","year":"2017","unstructured":"Pyrgelis A, Troncoso C, Cristofaro ED (2017) What does the crowd say about you? Evaluating aggregation-based location privacy. Proc Privacy Enhanc Technol 2017(4):156\u2013176. https:\/\/doi.org\/10.1515\/popets-2017-0043","journal-title":"Proc Privacy Enhanc Technol"},{"key":"7393_CR24","doi-asserted-by":"publisher","unstructured":"Pyrgelis A, Troncoso C, Cristofaro ED (2020) Measuring membership privacy on aggregate location time-series. In: Abstracts of the 2020 SIGMETRICS\/Performance joint international conference on measurement and modeling of computer systems, pp 1\u201328. ACM. https:\/\/doi.org\/10.1145\/3393691.3394200","DOI":"10.1145\/3393691.3394200"},{"key":"7393_CR25","doi-asserted-by":"publisher","unstructured":"Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography, pp 265\u2013284. Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/11681878_14","DOI":"10.1007\/11681878_14"},{"issue":"3\u20134","key":"7393_CR26","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Found Trends Theoret Comput Sci 9(3\u20134):211\u2013407","journal-title":"Found Trends Theoret Comput Sci"},{"key":"7393_CR27","doi-asserted-by":"publisher","unstructured":"Shokri R, Shmatikov V (2015) Privacy-preserving deep learning. CCS \u201915, pp 1310\u20131321. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2810103.2813687","DOI":"10.1145\/2810103.2813687"},{"key":"7393_CR28","doi-asserted-by":"publisher","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS '16), pp 308\u2013318. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"7393_CR29","unstructured":"Yousefpour A, Shilov I, Sablayrolles A, Testuggine D, Prasad K, Malek M, Nguyen J, Ghosh S, Bharadwaj A, Zhao J, Cormode G, Mironov I (2021) Opacus: user-friendly differential privacy library in pytorch. In: NeurIPS 2021 Workshop Privacy in Machine Learning"},{"issue":"29","key":"7393_CR30","first-page":"1069","volume":"12","author":"K Chaudhuri","year":"2011","unstructured":"Chaudhuri K, Monteleoni C, Sarwate AD (2011) Differentially private empirical risk minimization. J Mach Learn Res 12(29):1069\u20131109","journal-title":"J Mach Learn Res"},{"issue":"7","key":"7393_CR31","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","volume":"7","author":"PC Mahawaga Arachchige","year":"2020","unstructured":"Mahawaga Arachchige PC, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M (2020) Local differential privacy for deep learning. IEEE Internet Things J 7(7):5827\u20135842. https:\/\/doi.org\/10.1109\/JIOT.2019.2952146","journal-title":"IEEE Internet Things J"},{"key":"7393_CR32","unstructured":"McMahan HB, Andrew G, Erlingsson U, Chien S, Mironov I, Papernot N, Kairouz P (2018) A general approach to adding differential privacy to iterative training procedures. In: Advances in neural information processing systems (NeurIPS) Workshop on privacy preserving machine learning"},{"key":"7393_CR33","unstructured":"Carlini N, Tram\u00e8r F, Wallace E, Jagielski M, Herbert-Voss A, Lee K, Roberts A, Brown T, Song D, Erlingsson \u00da, Oprea A, Raffel C (2021) Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 21), pp 2633\u20132650. USENIX Association"},{"key":"7393_CR34","unstructured":"Yang Y, Gohari P, Topcu U (2021) On the privacy risks of deploying recurrent neural networks in machine learning. arXiv preprint arXiv:2110.03054"},{"key":"7393_CR35","doi-asserted-by":"publisher","unstructured":"Song C, Ristenpart T, Shmatikov V (2017) Machine learning models that remember too much. CCS \u201917, pp 587\u2013601. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3133956.3134077","DOI":"10.1145\/3133956.3134077"},{"key":"7393_CR36","unstructured":"Carlini N, Liu C, Erlingsson \u00da, Kos J, Song D (2019) The secret sharer: evaluating and testing unintended memorization in neural networks. In: 28th USENIX Security Symposium (USENIX Security 19), pp 267\u2013284. USENIX Association, Santa Clara, CA"},{"key":"7393_CR37","doi-asserted-by":"publisher","unstructured":"Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models. In: 2017 IEEE symposium on security and privacy (SP), pp 3\u201318. IEEE. https:\/\/doi.org\/10.1109\/sp.2017.41","DOI":"10.1109\/sp.2017.41"},{"key":"7393_CR38","unstructured":"McCandless D, Evans T, Quick M, Hollowood E, Miles C, Hampson D, Geere D (2021) World\u2019s biggest data breaches & hacks. https:\/\/www.informationisbeautiful.net\/visualizations\/worlds-biggest-data-breaches-hacks\/. Online; Accessed 11 March 2021"},{"key":"7393_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e","author":"A Wesolowski","year":"2014","unstructured":"Wesolowski A, Buckee CO, Bengtsson L, Wetter E, Lu X, Tatem AJ (2014) Commentary: containing the ebola outbreak - the potential and challenge of mobile network data. PLoS Currents. https:\/\/doi.org\/10.1371\/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e","journal-title":"PLoS Currents"},{"issue":"8","key":"7393_CR40","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"7393_CR41","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning"},{"issue":"11","key":"7393_CR42","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681. https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans Signal Process"},{"issue":"1","key":"7393_CR43","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","volume":"37","author":"H Hewamalage","year":"2021","unstructured":"Hewamalage H, Bergmeir C, Bandara K (2021) Recurrent neural networks for time series forecasting: current status and future directions. Int J Forecast 37(1):388\u2013427. https:\/\/doi.org\/10.1016\/j.ijforecast.2020.06.008","journal-title":"Int J Forecast"},{"key":"7393_CR44","doi-asserted-by":"publisher","unstructured":"Rogers R, Subramaniam S, Peng S, Durfee D, Lee S, Kancha SK, Sahay S, Ahammad P (2021) Linkedin\u2019s audience engagements API: A privacy preserving data analytics system at scale. Journal of Privacy and Confidentiality 11(3). https:\/\/doi.org\/10.29012\/jpc.782","DOI":"10.29012\/jpc.782"},{"key":"7393_CR45","unstructured":"Aktay A, Bavadekar S, Cossoul G, Davis J, Desfontaines D, Fabrikant A, Gabrilovich E, Gadepalli K, Gipson B, Guevara M et\u00a0al (2020) Google COVID-19 community mobility reports: anonymization process description (version 1.1). arXiv preprint arXiv:2004.04145"},{"key":"7393_CR46","doi-asserted-by":"crossref","unstructured":"Bergstra J, Yamins D, Cox DD (2013) Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: Proceedings of the 30th international conference on international conference on machine learning, ICML\u201913, pp I-115\u2013I-123. JMLR","DOI":"10.25080\/Majora-8b375195-003"},{"key":"7393_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05626-8","author":"I Rahimi","year":"2021","unstructured":"Rahimi I, Chen F, Gandomi AH (2021) A review on COVID-19 forecasting models. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05626-8","journal-title":"Neural Comput Appl"},{"key":"7393_CR48","doi-asserted-by":"publisher","first-page":"106181","DOI":"10.1016\/j.asoc.2020.106181","volume":"90","author":"OB Sezer","year":"2020","unstructured":"Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time series forecasting with deep learning: a systematic literature review: 2005\u20132019. Appl Soft Comput 90:106181. https:\/\/doi.org\/10.1016\/j.asoc.2020.106181","journal-title":"Appl Soft Comput"},{"key":"7393_CR49","doi-asserted-by":"publisher","first-page":"106269","DOI":"10.1016\/j.ijepes.2020.106269","volume":"123","author":"SF Stefenon","year":"2020","unstructured":"Stefenon SF, Ribeiro MHDM, Nied A, Mariani VC, dos Santos Coelho L, da Rocha DFM, Grebogi RB, de Barros Ruano A.E (2020) Wavelet group method of data handling for fault prediction in electrical power insulators. Int J Electric Power Energy Syst 123:106269. https:\/\/doi.org\/10.1016\/j.ijepes.2020.106269","journal-title":"Int J Electric Power Energy Syst"},{"key":"7393_CR50","doi-asserted-by":"publisher","first-page":"112869","DOI":"10.1016\/j.enconman.2020.112869","volume":"213","author":"SR Moreno","year":"2020","unstructured":"Moreno SR, da Silva RG, Mariani VC, dos Santos Coelho L (2020) Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Convers Manage 213:112869. https:\/\/doi.org\/10.1016\/j.enconman.2020.112869","journal-title":"Energy Convers Manage"},{"key":"7393_CR51","doi-asserted-by":"publisher","first-page":"102736","DOI":"10.1016\/j.jnca.2020.102736","volume":"168","author":"S Chen","year":"2020","unstructured":"Chen S, Fu A, Shen J, Yu S, Wang H, Sun H (2020) RNN-DP: a new differential privacy scheme base on recurrent neural network for dynamic trajectory privacy protection. J Netw Comput Appl 168:102736. https:\/\/doi.org\/10.1016\/j.jnca.2020.102736","journal-title":"J Netw Comput Appl"},{"key":"7393_CR52","doi-asserted-by":"publisher","DOI":"10.1186\/s42162-018-0025-3","author":"G Eibl","year":"2018","unstructured":"Eibl G, Bao K, Grassal PW, Bernau D, Schmeck H (2018) The influence of differential privacy on short term electric load forecasting. Energy Inform. https:\/\/doi.org\/10.1186\/s42162-018-0025-3","journal-title":"Energy Inform"},{"key":"7393_CR53","doi-asserted-by":"publisher","unstructured":"Imtiaz S, Horchidan SF, Abbas Z, Arsalan M, Chaudhry HN, Vlassov V (2020) Privacy preserving time-series forecasting of user health data streams. In: 2020 IEEE International conference on big data (Big Data). IEEE. https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378186","DOI":"10.1109\/bigdata50022.2020.9378186"},{"issue":"3","key":"7393_CR54","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3390\/mca26030056","volume":"26","author":"HH Arcolezi","year":"2021","unstructured":"Arcolezi HH, Cerna S, Guyeux C, Couchot JF (2021) Preserving geo-indistinguishability of the emergency scene to predict ambulance response time. Math Comput Appl 26(3):56. https:\/\/doi.org\/10.3390\/mca26030056","journal-title":"Math Comput Appl"},{"key":"7393_CR55","doi-asserted-by":"publisher","unstructured":"Arcolezi HH, Cerna S, Couchot JF, Guyeux C, Makhoul A (2022) Privacy-preserving prediction of victim\u2019s mortality and their need for transportation to health facilities. IEEE Trans Ind Inform 18(8):5592\u22125599. https:\/\/doi.org\/10.1109\/TII.2021.3123588","DOI":"10.1109\/TII.2021.3123588"},{"key":"7393_CR56","doi-asserted-by":"publisher","unstructured":"Soykan E.U, Bilgin Z, Ersoy M.A, Tomur E (2019) Differentially private deep learning for load forecasting on smart grid. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp 1\u20136. IEEE. https:\/\/doi.org\/10.1109\/gcwkshps45667.2019.9024520","DOI":"10.1109\/gcwkshps45667.2019.9024520"},{"key":"7393_CR57","doi-asserted-by":"crossref","unstructured":"Ouyang K, Shokri R, Rosenblum DS, Yang W (2018) A non-parametric generative model for human trajectories. IJCAI\u201918, pp 3812\u20133817. AAAI Press","DOI":"10.24963\/ijcai.2018\/530"},{"key":"7393_CR58","doi-asserted-by":"publisher","unstructured":"Mir D.J, Isaacman S, Caceres R, Martonosi M, Wright RN (2013) DP-WHERE: differentially private modeling of human mobility. In: 2013 IEEE international conference on big data. IEEE. https:\/\/doi.org\/10.1109\/bigdata.2013.6691626","DOI":"10.1109\/bigdata.2013.6691626"},{"key":"7393_CR59","doi-asserted-by":"publisher","unstructured":"Arcolezi HH, Couchot JF, Baala O, Contet JM, Al\u00a0Bouna B, Xiao X (2020) Mobility modeling through mobile data: generating an optimized and open dataset respecting privacy. In: 2020 international wireless communications and mobile computing (IWCMC), pp 1689\u20131694. https:\/\/doi.org\/10.1109\/IWCMC48107.2020.9148138","DOI":"10.1109\/IWCMC48107.2020.9148138"},{"issue":"6","key":"7393_CR60","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1109\/TITS.2017.2695438","volume":"19","author":"M Yin","year":"2018","unstructured":"Yin M, Sheehan M, Feygin S, Paiement JF, Pozdnoukhov A (2018) A generative model of urban activities from cellular data. IEEE Trans Intell Transp Syst 19(6):1682\u20131696. https:\/\/doi.org\/10.1109\/TITS.2017.2695438","journal-title":"IEEE Trans Intell Transp Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07393-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07393-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07393-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:18:17Z","timestamp":1744201097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07393-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,3]]},"references-count":60,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7393"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07393-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,3]]},"assertion":[{"value":"1 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animals research"}},{"value":"As this article does not contain any studies with human participants or animals, the informed consent is not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}