{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T10:42:25Z","timestamp":1748083345736},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this work, we propose the use of a genetic-algorithm-based attack against machine learning classifiers with the aim of \u2018stealing\u2019 users\u2019 biometric actigraphy profiles from health related sensor data. The target classification model uses daily actigraphy patterns for user identification. The biometric profiles are modeled as what we call <jats:italic>impersonator examples<\/jats:italic> which are generated based solely on the predictions\u2019 confidence score by repeatedly querying the target classifier. We conducted experiments in a black-box setting on a public dataset that contains actigraphy profiles from 55 individuals. The data consists of daily motion patterns recorded with an actigraphy device. These patterns can be used as biometric profiles to identify each individual. Our attack was able to generate examples capable of impersonating a target user with a success rate of 94.5<jats:italic>%<\/jats:italic>. Furthermore, we found that the <jats:italic>impersonator examples<\/jats:italic> have high transferability to other classifiers trained with the same training set. We also show that the generated biometric profiles have a close resemblance to the ground truth profiles which can lead to sensitive data exposure, like revealing the time of the day an individual wakes-up and goes to bed.<\/jats:p>","DOI":"10.1007\/s10916-020-01646-y","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T04:12:36Z","timestamp":1600143156000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data"],"prefix":"10.1007","volume":"44","author":[{"given":"Enrique","family":"Garcia-Ceja","sequence":"first","affiliation":[]},{"given":"Brice","family":"Morin","sequence":"additional","affiliation":[]},{"given":"Anton","family":"Aguilar-Rivera","sequence":"additional","affiliation":[]},{"given":"Michael Alexander","family":"Riegler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"1646_CR1","doi-asserted-by":"crossref","unstructured":"Al-Naffakh N., Clarke N., Li F.: Continuous User Authentication Using Smartwatch Motion Sensor Data. In: (Gal-Oz N., Lewis P. R., Eds.) Trust Management XII. Springer International Publishing, Cham, 2018, pp 15\u201328","DOI":"10.1007\/978-3-319-95276-5_2"},{"key":"1646_CR2","unstructured":"Alegre F., Vipperla R., Evans N., Fauve B.: On the vulnerability of automatic speaker recognition to spoofing attacks with artificial signals.. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012, pp 36\u201340"},{"key":"1646_CR3","doi-asserted-by":"crossref","unstructured":"Alzantot M., Sharma Y., Chakraborty S., Srivastava M. (2018) Genattack: Practical black-box attacks with gradient-free optimization. arXiv:1805.11090","DOI":"10.1145\/3321707.3321749"},{"key":"1646_CR4","unstructured":"Avci A., Bosch S., Marin-Perianu M., Marin-Perianu R., Havinga P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey.. In: 23th International conference on architecture of computing systems 2010, 2010, pp 1\u201310. VDE"},{"issue":"2","key":"1646_CR5","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/0004-3702(93)90071-I","volume":"61","author":"A Bertoni","year":"1993","unstructured":"Bertoni A., Dorigo M.: Implicit parallelism in genetic algorithms. Artif. Intell. 61(2):307\u2013314, 1993","journal-title":"Artif. Intell."},{"key":"1646_CR6","doi-asserted-by":"crossref","unstructured":"Biggio B., Corona I., Maiorca D., Nelson B., \u0160rndi\u00ed N., Laskov P., Giacinto G., Roli F.: Evasion attacks against machine learning at test time. In: (Blockeel H., Kersting K., Nijssen S., \u017eelezn\u00fd F., Eds.) Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, 2013, pp 387\u2013402","DOI":"10.1007\/978-3-642-40994-3_25"},{"issue":"5","key":"1646_CR7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MSP.2015.2426728","volume":"32","author":"B Biggio","year":"2015","unstructured":"Biggio B., Fumera G., Russu P., Didaci L., Roli F.: Adversarial biometric recognition: A review on biometric system security from the adversarial machine-learning perspective. IEEE Signal Processing Magazine 32(5):31\u201341, 2015. https:\/\/doi.org\/10.1109\/MSP.2015.2426728","journal-title":"IEEE Signal Processing Magazine"},{"key":"1646_CR8","doi-asserted-by":"publisher","unstructured":"Biggio B., Roli F. (2018) Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition 84. https:\/\/doi.org\/10.1016\/j.patcog.2018.07.023. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320318302565","DOI":"10.1016\/j.patcog.2018.07.023"},{"issue":"1","key":"1646_CR9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L.: Random forests. Machine Learning 45 (1): 5\u201332, 2001","journal-title":"Machine Learning"},{"key":"1646_CR10","doi-asserted-by":"publisher","unstructured":"Buriro A., Acker R. V., Crispo B., Mahboob A.: Airsign: A gesture-based smartwatch user authentication.. In: 2018 International Carnahan Conference on Security Technology (ICCST), 2018, pp 1\u20135. https:\/\/doi.org\/10.1109\/CCST.2018.8585571","DOI":"10.1109\/CCST.2018.8585571"},{"key":"1646_CR11","doi-asserted-by":"crossref","unstructured":"Buriro A., Crispo B., Eskandri M., Gupta S., Mahboob A., Van Acker R.: SNAPAUTH: A gesture-based unobtrusive smartwatch user authentication scheme.. In: International Workshop on Emerging Technologies for Authorization and Authentication. Springer, 2018, pp 30\u201337","DOI":"10.1007\/978-3-030-04372-8_3"},{"issue":"1","key":"1646_CR12","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10479-019-03343-7","volume":"287","author":"Z Drezner","year":"2020","unstructured":"Drezner Z., Drezner T. D.: Biologically inspired parent selection in genetic algorithms. Ann. Oper. Res. 287(1):161\u2013183, 2020","journal-title":"Ann. Oper. Res."},{"issue":"1","key":"1646_CR13","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M., Cernadas E., Barro S., Amorim D.: Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research 15(1):3133\u20133181, 2014","journal-title":"The Journal of Machine Learning Research"},{"issue":"2","key":"1646_CR14","first-page":"143","volume":"22","author":"V Filipovi\u0107","year":"2012","unstructured":"Filipovi\u0107 V.: Fine-grained tournament selection operator in genetic algorithms. Computing and Informatics 22(2):143\u2013161, 2012","journal-title":"Computing and Informatics"},{"issue":"4","key":"1646_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1518-8","volume":"44","author":"D Fozoonmayeh","year":"2020","unstructured":"Fozoonmayeh D., Le H. V., Wittfoth E., Geng C., Ha N., Wang J., Vasilenko M., Ahn Y., Woodbridge D.M.K.: A scalable smartwatch-based medication intake detection system using distributed machine learning. J. Med. Syst. 44(4):1\u201314, 2020","journal-title":"J. Med. Syst."},{"key":"1646_CR16","doi-asserted-by":"crossref","unstructured":"Fredrikson M., Jha S., Ristenpart T.: Model inversion attacks that exploit confidence information and basic countermeasures.. In: Proceedings of the 22nd ACM SIGSAC Conference on computer and communications security. ACM, 2015, pp 1322\u20131333","DOI":"10.1145\/2810103.2813677"},{"issue":"10","key":"1646_CR17","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.1016\/j.cviu.2013.06.003","volume":"117","author":"J Galbally","year":"2013","unstructured":"Galbally J., Ross A., Gomez-Barrero M., Fierrez J., Ortega-Garcia J.: Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms. Comput. Vis. Image Underst. 117(10):1512\u20131525, 2013","journal-title":"Comput. Vis. Image Underst."},{"key":"1646_CR18","doi-asserted-by":"crossref","unstructured":"Galv\u00e1n-Tejada C. E., Zanella-Calzada L. A., Gamboa-Rosales H., Galv\u00e1n-Tejada J. I., Ch\u00e1vez-Lamas N. M., Gracia-Cort\u00e9s M., Magallanes-Quintanar R., Celaya-Padilla J. M., et al. (2019) Depression episodes detection in unipolar and bipolar patients: A methodology with feature extraction and feature selection with genetic algorithms using activity motion signal as information source. Mob. Inf. Syst. 2019","DOI":"10.1155\/2019\/8269695"},{"key":"1646_CR19","doi-asserted-by":"crossref","unstructured":"Garcia-Ceja E., Morin B.: User recognition based on daily actigraphy patterns.. In: 2019 International Conference on Trust Management (IFIPTM). Springer, 2019","DOI":"10.1007\/978-3-030-33716-2_6"},{"key":"1646_CR20","doi-asserted-by":"publisher","unstructured":"Garcia-Ceja E., Riegler M., Jakobsen P., rresen J.T., Nordgreen T., Oedegaard K.J., Fasmer O.B.: Depresjon: A motor activity database of depression episodes in unipolar and bipolar patients.. In: Proceedings of the 9th ACM on Multimedia Systems Conference, MMSys\u201918. ACM, New York, 2018, pp 472\u2013477. https:\/\/doi.org\/10.1145\/3204949.3208125","DOI":"10.1145\/3204949.3208125"},{"key":"1646_CR21","doi-asserted-by":"crossref","unstructured":"Garcia-Ceja E., Riegler M., Jakobsen P., Torresen J., Nordgreen T., Oedegaard K. J., Fasmer O. B.: Motor activity based classification of depression in unipolar and bipolar patients.. In: 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2018, pp 316\u2013321","DOI":"10.1109\/CBMS.2018.00062"},{"key":"1646_CR22","doi-asserted-by":"crossref","unstructured":"Garcia-Ceja E., Riegler M., Nordgreen T., Jakobsen P., Oedegaard K. J., Torresen J. (2018) Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing","DOI":"10.1016\/j.pmcj.2018.09.003"},{"issue":"6","key":"1646_CR23","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s10916-020-01565-y","volume":"44","author":"SK Ghosh","year":"2020","unstructured":"Ghosh S. K., Tripathy R. K., Paternina M. R. A., Arrieta J. J., Zamora-Mendez A., Naik G. R.: Detection of atrial fibrillation from single lead ecg signal using multirate cosine filter bank and deep neural network. J. Medical Syst. 44(6):114, 2020","journal-title":"J. Medical Syst."},{"key":"1646_CR24","unstructured":"Goodfellow I. J., Shlens J., Szegedy C. (2014) Explaining and harnessing adversarial examples. arXiv:1412.6572"},{"key":"1646_CR25","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.sleep.2018.03.023","volume":"47","author":"R Gruber","year":"2018","unstructured":"Gruber R., Somerville G., Wells S., Keskinel D., Santisteban J. A.: An actigraphic study of the sleep patterns of younger and older school-age children. Sleep medicine 47:117\u2013125, 2018","journal-title":"Sleep medicine"},{"key":"1646_CR26","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","volume":"83","author":"Z Hu","year":"2018","unstructured":"Hu Z., Tang J., Wang Z., Zhang K., Zhang L., Sun Q.: Deep learning for image-based cancer detection and diagnosis- a survey. Pattern Recogn. 83:134\u2013149, 2018","journal-title":"Pattern Recogn."},{"issue":"P2","key":"1646_CR27","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.patrec.2015.07.004","volume":"68","author":"A Jain","year":"2015","unstructured":"Jain A., Kanhangad V.: Exploring orientation and accelerometer sensor data for personal authentication in smartphones using touchscreen gestures. Pattern Recogn. Lett. 68(P2):351\u2013360, 2015. https:\/\/doi.org\/10.1016\/j.patrec.2015.07.004","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"1646_CR28","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TIFS.2017.2738611","volume":"13","author":"N Khamsemanan","year":"2018","unstructured":"Khamsemanan N., Nattee C., Jianwattanapaisarn N.: Human identification from freestyle walks using posture-based gait feature. IEEE Transactions on Information Forensics and Security 13 (1): 119\u2013128, 2018. https:\/\/doi.org\/10.1109\/TIFS.2017.2738611","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"1646_CR29","doi-asserted-by":"crossref","unstructured":"Kohli N., Yadav D., Vatsa M., Singh R., Noore A.: Detecting medley of iris spoofing attacks using desist.. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2016, pp 1\u20136","DOI":"10.1109\/BTAS.2016.7791168"},{"issue":"5","key":"1646_CR30","first-page":"889","volume":"19","author":"WY Lin","year":"2003","unstructured":"Lin W. Y., Lee W. Y., Hong T. P.: Adapting crossover and mutation rates in genetic algorithms. J. Inf. Sci. Eng. 19(5):889\u2013903, 2003","journal-title":"J. Inf. Sci. Eng."},{"key":"1646_CR31","doi-asserted-by":"publisher","first-page":"12103","DOI":"10.1109\/ACCESS.2018.2805680","volume":"6","author":"Q Liu","year":"2018","unstructured":"Liu Q., Li P., Zhao W., Cai W., Yu S., Leung V. C. M.: A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE Access 6: 12103\u201312117, 2018. https:\/\/doi.org\/10.1109\/ACCESS.2018.2805680","journal-title":"IEEE Access"},{"key":"1646_CR32","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jisa.2017.10.002","volume":"37","author":"A Mahfouz","year":"2017","unstructured":"Mahfouz A., Mahmoud T. M., Eldin A. S.: A survey on behavioral biometric authentication on smartphones. Journal of Information Security and Applications 37:28\u201337, 2017","journal-title":"Journal of Information Security and Applications"},{"issue":"9","key":"1646_CR33","first-page":"16","volume":"5","author":"A Mishra","year":"2017","unstructured":"Mishra A.: Nature inspired algorithms: a survey of the state of the art. Int. J. 5(9):16\u201321, 2017","journal-title":"Int. J."},{"key":"1646_CR34","doi-asserted-by":"publisher","unstructured":"Mufandaidza M. P., Ramotsoela T. D., Hancke G. P.: Continuous user authentication in smartphones using gait analysis.. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial electronics society, 2018, pp 4656\u20134661. https:\/\/doi.org\/10.1109\/IECON.2018.8591193","DOI":"10.1109\/IECON.2018.8591193"},{"key":"1646_CR35","doi-asserted-by":"crossref","unstructured":"Nguyen A.M., Yosinski J., Clune J. (2014) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. arXiv:1412.1897","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"34","key":"1646_CR36","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.12988\/ces.2018.84166","volume":"11","author":"N Ortiz","year":"2018","unstructured":"Ortiz N., Bele\u00f1o R., Moreno R., Mauledeoux M., S\u00e3nchez O.: Survey of biometric pattern recognition via machine learning techniques. Contemp. Eng. Sci. 11(34):1677\u20131694, 2018","journal-title":"Contemp. Eng. Sci."},{"key":"1646_CR37","doi-asserted-by":"crossref","unstructured":"Papernot N., McDaniel P., Goodfellow I., Jha S., Celik Z. B., Swami A.: Practical black-box attacks against machine learning.. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security. ACM, 2017, pp 506\u2013519","DOI":"10.1145\/3052973.3053009"},{"key":"1646_CR38","unstructured":"Papernot N., McDaniel P., Sinha A., Wellman M. (2016) Towards the science of security and privacy in machine learning. arXiv:1611.03814"},{"issue":"4","key":"1646_CR39","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/MSP.2016.2555335","volume":"33","author":"VM Patel","year":"2016","unstructured":"Patel V. M., Chellappa R., Chandra D., Barbello B.: Continuous user authentication on mobile devices: Recent progress and remaining challenges. IEEE Signal Process. Mag. 33(4):49\u201361, 2016. https:\/\/doi.org\/10.1109\/MSP.2016.2555335","journal-title":"IEEE Signal Process. Mag."},{"key":"1646_CR40","unstructured":"Pelikan M., Goldberg D. E., Cant\u00fa-Paz E.: Boa: The bayesian optimization algorithm.. In: Proceedings of the 1st annual conference on genetic and evolutionary computation, vol 1. Morgan Kaufmann Publishers Inc, 1999, pp 525\u2013532"},{"issue":"4","key":"1646_CR41","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1049\/el.2012.4173","volume":"49","author":"L Pereira","year":"2013","unstructured":"Pereira L., Pinheiro H., Cavalcanti G. D., Ren T. I.: Spatial surface coarseness analysis: technique for fingerprint spoof detection. Electronics letters 49(4):260\u2013261, 2013","journal-title":"Electronics letters"},{"key":"1646_CR42","doi-asserted-by":"crossref","unstructured":"Pyrgelis A., Troncoso C., De Cristofaro E. (2017) Knock knock, who\u2019s there? membership inference on aggregate location data. arXiv:1708.06145","DOI":"10.14722\/ndss.2018.23183"},{"key":"1646_CR43","unstructured":"Quiring E., Maier A., Rieck K. (2019) Misleading authorship attribution of source code using adversarial learning. arXiv:1905.12386"},{"issue":"1","key":"1646_CR44","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s13670-019-0275-3","volume":"8","author":"AK Rao","year":"2019","unstructured":"Rao A. K.: Wearable sensor technology to measure physical activity (pa) in the elderly. Current Geriatrics Reports 8(1):55\u201366, 2019","journal-title":"Current Geriatrics Reports"},{"issue":"4","key":"1646_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-020-1541-9","volume":"44","author":"J Rocha","year":"2020","unstructured":"Rocha J., Cunha A., Mendon\u010ba A. M.: Conventional filtering versus u-net based models for pulmonary nodule segmentation in ct images. J. Med. Syst. 44(4):1\u20138, 2020","journal-title":"J. Med. Syst."},{"issue":"4","key":"1646_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v053.i04","volume":"53","author":"L Scrucca","year":"2013","unstructured":"Scrucca L.: GA: A package for genetic algorithms in R. J. Stat. Softw. 53 (4): 1\u201337, 2013. http:\/\/www.jstatsoft.org\/v53\/i04\/","journal-title":"J. Stat. Softw."},{"issue":"1","key":"1646_CR47","doi-asserted-by":"publisher","first-page":"187","DOI":"10.32614\/RJ-2017-008","volume":"9","author":"L Scrucca","year":"2017","unstructured":"Scrucca L.: On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution. The R Journal 9 (1): 187\u2013206, 2017. https:\/\/journal.r-project.org\/archive\/2017\/RJ-2017-008","journal-title":"The R Journal"},{"key":"1646_CR48","doi-asserted-by":"publisher","unstructured":"Sharif M., Bhagavatula S., Bauer L., Reiter M.K.: Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition.. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, CCS \u201916. ACM, New York, 2016, pp 1528\u20131540. https:\/\/doi.org\/10.1145\/2976749.2978392. Event-place: Vienna, Austria","DOI":"10.1145\/2976749.2978392"},{"key":"1646_CR49","unstructured":"Sharif M., Bhagavatula S., Bauer L., Reiter M. K. (2017) Adversarial generative nets: Neural network attacks on state-of-the-art face recognition. arXiv:1801.00349"},{"issue":"1","key":"1646_CR50","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/TIFS.2017.2737969","volume":"13","author":"C Shen","year":"2018","unstructured":"Shen C., Li Y., Chen Y., Guan X., Maxion R. A.: Performance analysis of multi-motion sensor behavior for active smartphone authentication. IEEE Transactions on Information Forensics and Security 13 (1): 48\u201362, 2018. https:\/\/doi.org\/10.1109\/TIFS.2017.2737969","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"1646_CR51","doi-asserted-by":"crossref","unstructured":"Shokri R., Stronati M., Song C., Shmatikov V.: Membership inference attacks against machine learning models.. In: 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 2017, pp 3\u201318","DOI":"10.1109\/SP.2017.41"},{"key":"1646_CR52","doi-asserted-by":"crossref","unstructured":"Song C., Ristenpart T., Shmatikov V.: Machine learning models that remember too much.. In: Proceedings of the 2017 ACM SIGSAC Conference on computer and communications security, 2017, pp 587\u2013601","DOI":"10.1145\/3133956.3134077"},{"key":"1646_CR53","doi-asserted-by":"publisher","unstructured":"Su J., Vargas D. V., Sakurai K. (2019) One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 1\u20131, https:\/\/doi.org\/10.1109\/TEVC.2019.2890858","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"1646_CR54","unstructured":"Tram\u00e8r F., Kurakin A., Papernot N., Goodfellow I., Boneh D., McDaniel P. (2017) Ensemble adversarial training: Attacks and defenses. arXiv:1705.07204"},{"key":"1646_CR55","unstructured":"Tram\u00e8r F., Zhang F., Juels A., Reiter M. K., Ristenpart T.: Stealing machine learning models via prediction apis.. In: 25th USENIX Security Symposium (USENIX Security 16), 2016, pp 601\u2013618"},{"key":"1646_CR56","doi-asserted-by":"crossref","unstructured":"Xi X., Keogh E., Shelton C., Wei L., Ratanamahatana C. A.: Fast time series classification using numerosity reduction.. In: Proceedings of the 23rd international conference on machine learning, 2006, pp 1033\u20131040","DOI":"10.1145\/1143844.1143974"},{"key":"1646_CR57","doi-asserted-by":"publisher","unstructured":"Yang J., Li Y., Xie M.: Motionauth: Motion-based authentication for wrist worn smart devices.. In: 2015 IEEE International conference on pervasive computing and communication workshops (PerCom Workshops), 2015, pp 550\u2013555. https:\/\/doi.org\/10.1109\/PERCOMW.2015.7134097","DOI":"10.1109\/PERCOMW.2015.7134097"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-020-01646-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-020-01646-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-020-01646-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:18:14Z","timestamp":1631665094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-020-01646-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":57,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["1646"],"URL":"https:\/\/doi.org\/10.1007\/s10916-020-01646-y","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]},"assertion":[{"value":"22 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"All authors declare that they do not have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}],"article-number":"187"}}