{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T23:15:36Z","timestamp":1763507736143,"version":"3.41.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:00:00Z","timestamp":1584489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2020,3,18]]},"abstract":"<jats:p>With the rising popularity of wearable devices and sensors, shielding Body Area Networks (BANs) from eavesdroppers has become an urgent problem to solve. Since the conventional key distribution systems are too onerous for resource-constrained wearable sensors, researchers are pursuing a new light-weight key generation approach that enables two wearable devices attached at different locations of the user body to generate an identical key simultaneously simply from their independent observations of user gait. A key challenge for such gait-based key generation lies in matching the bits of the keys generated by independent devices despite the noisy sensor measurements, especially when the devices are located far apart on the body affected by different sources of noise. To address the challenge, we propose a novel machine learning framework, called Auto-Key, that uses an autoencoder to help one device predict the gait observations at another distant device attached to the same body and generate the key using the predicted sensor data. We prototype the proposed method and evaluate it using a public acceleration dataset collected from 15 real subjects wearing accelerometers attached to seven different locations of the body. Our results show that, on average, Auto-Key increases the matching rate of independently generated bits from two sensors attached at two different locations by 16.5%, which speeds up the successful generation of fully-matching symmetric keys at independent wearable sensors by a factor of 1.9. In the proposed framework, a subject-specific model can be trained with 50% fewer data and 88% less time by retraining a pre-trained general model when compared to training a new model from scratch. The reduced training complexity makes Auto-Key more practical for edge computing, which provides better privacy protection to biometric and behavioral data compared to cloud-based training.<\/jats:p>","DOI":"10.1145\/3381004","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T18:54:31Z","timestamp":1584557671000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Auto-Key"],"prefix":"10.1145","volume":"4","author":[{"given":"Yuezhong","family":"Wu","sequence":"first","affiliation":[{"name":"The University of New South Wales, School of Computer Science and Engineering, Sydney, NSW, Australia, CSIRO-Data61, Australia"}]},{"given":"Qi","family":"Lin","sequence":"additional","affiliation":[{"name":"The University of New South Wales, School of Computer Science and Engineering, Sydney, NSW, Australia, CSIRO-Data61, Australia"}]},{"given":"Hong","family":"Jia","sequence":"additional","affiliation":[{"name":"The University of New South Wales, School of Computer Science and Engineering, Sydney, NSW, Australia, CSIRO-Data61, Australia"}]},{"given":"Mahbub","family":"Hassan","sequence":"additional","affiliation":[{"name":"The University of New South Wales, School of Computer Science and Engineering, Sydney, NSW, Australia, CSIRO-Data61, Australia"}]},{"given":"Wen","family":"Hu","sequence":"additional","affiliation":[{"name":"The University of New South Wales, School of Computer Science and Engineering, Sydney, NSW, Australia, CSIRO-Data61, Australia"}]}],"member":"320","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Tensorflow: A system for large-scale machine learning. In 12th [USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265--283.","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . Tensorflow: A system for large-scale machine learning. In 12th [USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265--283. Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th [USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265--283."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700332"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Tim Althoff Jennifer L Hicks Abby C King Scott L Delp Jure Leskovec etal 2017. Large-scale physical activity data reveal worldwide activity inequality. Nature 547 7663 (2017) 336.  Tim Althoff Jennifer L Hicks Abby C King Scott L Delp Jure Leskovec et al. 2017. Large-scale physical activity data reveal worldwide activity inequality. Nature 547 7663 (2017) 336.","DOI":"10.1038\/nature23018"},{"key":"e_1_2_1_4_1","unstructured":"Sarath Chandar AP Stanislas Lauly Hugo Larochelle Mitesh Khapra Balaraman Ravindran Vikas C Raykar and Amrita Saha. 2014. An autoencoder approach to learning bilingual word representations. In Advances in Neural Information Processing Systems. 1853--1861.  Sarath Chandar AP Stanislas Lauly Hugo Larochelle Mitesh Khapra Balaraman Ravindran Vikas C Raykar and Amrita Saha. 2014. An autoencoder approach to learning bilingual word representations. In Advances in Neural Information Processing Systems. 1853--1861."},{"volume-title":"Noise reduction in speech processing","author":"Benesty Jacob","key":"e_1_2_1_5_1","unstructured":"Jacob Benesty , Jingdong Chen , Yiteng Huang , and Israel Cohen . 2009. Pearson correlation coefficient . In Noise reduction in speech processing . Springer , 1--4. Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation coefficient. In Noise reduction in speech processing. Springer, 1--4."},{"key":"#cr-split#-e_1_2_1_6_1.1","doi-asserted-by":"crossref","unstructured":"A. Bruesch L. Nguyen D. Sch\u00fcrmann S. Sigg and L. C. Wolf. 2019. Security Properties of Gait for Mobile Device Pairing. IEEE Transactions on Mobile Computing (2019) 1--1. https:\/\/doi.org\/10.1109\/TMC.2019.2897933 10.1109\/TMC.2019.2897933","DOI":"10.1109\/TMC.2019.2897933"},{"key":"#cr-split#-e_1_2_1_6_1.2","doi-asserted-by":"crossref","unstructured":"A. Bruesch L. Nguyen D. Sch\u00fcrmann S. Sigg and L. C. Wolf. 2019. Security Properties of Gait for Mobile Device Pairing. IEEE Transactions on Mobile Computing (2019) 1--1. https:\/\/doi.org\/10.1109\/TMC.2019.2897933","DOI":"10.1109\/TMC.2019.2897933"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21726-5_21"},{"volume-title":"Deep Learning","author":"Goodfellow Ian","key":"e_1_2_1_8_1","unstructured":"Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep Learning . MIT Press . http:\/\/www.deeplearningbook.org. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http:\/\/www.deeplearningbook.org."},{"volume-title":"Deep Learning with Keras","author":"Gulli Antonio","key":"e_1_2_1_9_1","unstructured":"Antonio Gulli and Sujit Pal . 2017. Deep Learning with Keras . Packt Publishing Ltd . Antonio Gulli and Sujit Pal. 2017. Deep Learning with Keras. Packt Publishing Ltd."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487860"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2013.6567031"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1614320.1614356"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10623-005-6343-z"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1800171"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.02.006"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302506.3310406"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2012.2206385"},{"key":"e_1_2_1_19_1","unstructured":"Xugang Lu Yu Tsao Shigeki Matsuda and Chiori Hori. 2013. Speech enhancement based on deep denoising autoencoder.. In Interspeech. 436--440.  Xugang Lu Yu Tsao Shigeki Matsuda and Chiori Hori. 2013. Speech enhancement based on deep denoising autoencoder.. In Interspeech. 436--440."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.121313.00064"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2017.2686855"},{"key":"e_1_2_1_22_1","first-page":"290","article-title":"Gait as a total pattern of movement: Including a bibliography on gait","volume":"46","author":"Murray M Pat","year":"1967","unstructured":"M Pat Murray . 1967 . Gait as a total pattern of movement: Including a bibliography on gait . American Journal of Physical Medicine & Rehabilitation 46 , 1 (1967), 290 -- 333 . M Pat Murray. 1967. Gait as a total pattern of movement: Including a bibliography on gait. American Journal of Physical Medicine & Rehabilitation 46, 1 (1967), 290--333.","journal-title":"American Journal of Physical Medicine & Rehabilitation"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508859.2516658"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2017.7917865"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2731979"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/sec.973"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/BSN.2017.7936042"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Lisa Torrey and Jude Shavlik. 2010. Transfer learning. In Handbook of research on machine learning applications and trends: algorithms methods and techniques. IGI Global 242--264.  Lisa Torrey and Jude Shavlik. 2010. Transfer learning. In Handbook of research on machine learning applications and trends: algorithms methods and techniques. IGI Global 242--264.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"e_1_2_1_33_1","volume-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J. Mach. Learn. Res. 11 (Dec","author":"Vincent Pascal","year":"2010","unstructured":"Pascal Vincent , Hugo Larochelle , Isabelle Lajoie , Yoshua Bengio , and Pierre-Antoine Manzagol . 2010. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J. Mach. Learn. Res. 11 (Dec . 2010 ), 3371--3408. http:\/\/dl.acm.org\/citation.cfm?id=1756006.1953039 Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J. Mach. Learn. Res. 11 (Dec. 2010), 3371--3408. http:\/\/dl.acm.org\/citation.cfm?id=1756006.1953039"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2012.144"},{"key":"#cr-split#-e_1_2_1_35_1.1","doi-asserted-by":"crossref","unstructured":"W. Xu G. Revadigar C. Luo N. Bergmann and W. Hu. 2016. Walkie-Talkie: Motion-Assisted Automatic Key Generation for Secure On-Body Device Communication. In 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 1--12. https:\/\/doi.org\/10.1109\/IPSN.2016.7460726 10.1109\/IPSN.2016.7460726","DOI":"10.1109\/IPSN.2016.7460726"},{"key":"#cr-split#-e_1_2_1_35_1.2","doi-asserted-by":"crossref","unstructured":"W. Xu G. Revadigar C. Luo N. Bergmann and W. Hu. 2016. Walkie-Talkie: Motion-Assisted Automatic Key Generation for Secure On-Body Device Communication. In 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 1--12. https:\/\/doi.org\/10.1109\/IPSN.2016.7460726","DOI":"10.1109\/IPSN.2016.7460726"},{"volume-title":"How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27","author":"Yosinski Jason","key":"e_1_2_1_36_1","unstructured":"Jason Yosinski , Jeff Clune , Yoshua Bengio , and Hod Lipson . 2014. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 3320--3328. http:\/\/papers.nips.cc\/paper\/5347-how-transferable-are-features-in-deep-neural-networks.pdf Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 3320--3328. http:\/\/papers.nips.cc\/paper\/5347-how-transferable-are-features-in-deep-neural-networks.pdf"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2521718"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2010.5462231"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_62"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2013.6567032"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3381004","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3381004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:58Z","timestamp":1750203898000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3381004"}},"subtitle":["Using Autoencoder to Speed Up Gait-based Key Generation in Body Area Networks"],"short-title":[],"issued":{"date-parts":[[2020,3,18]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,3,18]]}},"alternative-id":["10.1145\/3381004"],"URL":"https:\/\/doi.org\/10.1145\/3381004","relation":{},"ISSN":["2474-9567"],"issn-type":[{"type":"electronic","value":"2474-9567"}],"subject":[],"published":{"date-parts":[[2020,3,18]]},"assertion":[{"value":"2020-03-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}