Littérature scientifique sur le sujet « Privacy preserving recognition »
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Articles de revues sur le sujet "Privacy preserving recognition"
Chamikara, M. A. P., P. Bertok, I. Khalil, D. Liu et S. Camtepe. « Privacy Preserving Face Recognition Utilizing Differential Privacy ». Computers & ; Security 97 (octobre 2020) : 101951. http://dx.doi.org/10.1016/j.cose.2020.101951.
Texte intégralLi, Yuancheng, Yimeng Wang et Daoxing Li. « Privacy-preserving lightweight face recognition ». Neurocomputing 363 (octobre 2019) : 212–22. http://dx.doi.org/10.1016/j.neucom.2019.07.039.
Texte intégralWoubie, Abraham, et Tom Backstrom. « Federated Learning for Privacy-Preserving Speaker Recognition ». IEEE Access 9 (2021) : 149477–85. http://dx.doi.org/10.1109/access.2021.3124029.
Texte intégralXiang, Can, Chunming Tang, Yunlu Cai et Qiuxia Xu. « Privacy-preserving face recognition with outsourced computation ». Soft Computing 20, no 9 (8 juillet 2015) : 3735–44. http://dx.doi.org/10.1007/s00500-015-1759-5.
Texte intégralXu, Honghui, Zhipeng Cai et Wei Li. « Privacy-Preserving Mechanisms for Multi-Label Image Recognition ». ACM Transactions on Knowledge Discovery from Data 16, no 4 (31 août 2022) : 1–21. http://dx.doi.org/10.1145/3491231.
Texte intégralWang, Lin, Chuan Zhao, Kun Zhao, Bo Zhang, Shan Jing, Zhenxiang Chen et Kuiheng Sun. « Privacy-Preserving Collaborative Computation for Human Activity Recognition ». Security and Communication Networks 2022 (28 février 2022) : 1–8. http://dx.doi.org/10.1155/2022/9428610.
Texte intégralKumar, Kambala Vijaya, et Jonnadula Harikiran. « Privacy preserving human activity recognition framework using an optimized prediction algorithm ». IAES International Journal of Artificial Intelligence (IJ-AI) 11, no 1 (1 mars 2022) : 254. http://dx.doi.org/10.11591/ijai.v11.i1.pp254-264.
Texte intégralWang, Yinggui, Jian Liu, Man Luo, Le Yang et Li Wang. « Privacy-Preserving Face Recognition in the Frequency Domain ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 3 (28 juin 2022) : 2558–66. http://dx.doi.org/10.1609/aaai.v36i3.20157.
Texte intégralZapechnikov, Sergey. « Contemporary trends in privacy-preserving data pattern recognition ». Procedia Computer Science 190 (2021) : 838–44. http://dx.doi.org/10.1016/j.procs.2021.06.098.
Texte intégralMa, Zhuo, Yang Liu, Ximeng Liu, Jianfeng Ma et Kui Ren. « Lightweight Privacy-Preserving Ensemble Classification for Face Recognition ». IEEE Internet of Things Journal 6, no 3 (juin 2019) : 5778–90. http://dx.doi.org/10.1109/jiot.2019.2905555.
Texte intégralThèses sur le sujet "Privacy preserving recognition"
PONTES, BRUNO SILVA. « HUMAN POSTURE RECOGNITION PRESERVING PRIVACY : A CASE STUDY USING A LOW RESOLUTION ARRAY THERMAL SENSOR ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=29776@1.
Texte intégralO reconhecimento de posturas é um dos desafios para o sensoriamento humano, que auxilia no acompanhamento de pessoas em ambientes de moradia assistidos. Estes ambientes, por sua vez, auxiliam médicos no diagnóstico de saúde de seus pacientes, principalmente através do reconhecimento de atividades do dia a dia em tempo real, que é visto na área médica como uma das melhores formas de antecipar situações críticas de saúde. Além disso, o envelhecimento da população mundial, escassez de recursos em hospitais para atender todas as pessoas e aumento dos custos de assistência médica impulsionam o desenvolvimento de sistemas para apoiar os ambientes de moradia assistidos. Preservar a privacidade nestes ambientes monitorados por sensores é um fator crítico para a aceitação do usuário, por isso há uma demanda em soluções que não requerem imagens. Este trabalho evidencia o uso de um sensor térmico de baixa resolução no sensoriamento humano, mostrando que é viável detectar a presença e reconhecer posturas humanas, usando somente os dados deste sensor.
Postures recognition is one of the human sensing challenges, that helps ambient assisted livings in people accompanying. On the other hand, these ambients assist doctors in the diagnosis of their patients health, mainly through activities of daily livings real time recognition, which is seen in the medical field as one of the best ways to anticipate critical health situations. In addition, the world s population aging, lack of hospital resources to meet all people and increased health care costs drive the development of systems to support ambient assisted livings. Preserving privacy in these ambients monitored by sensors is a critical factor for user acceptance, so there is a demand for solutions that does not requires images. This work demonstrates the use of a low resolution thermal array sensor in human sensing, showing that it is feasible to detect the presence and to recognize human postures, using only the data of this sensor.
Giulia, Droandi. « Secure Processing of Biometric Signals in Malicious Setting ». Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1061228.
Texte intégralWang, Yongjin. « Changeable and Privacy Preserving Face Recognition ». Thesis, 2010. http://hdl.handle.net/1807/26390.
Texte intégral(8782193), Tyler Stephen Phillips. « Privacy-Preserving Facial Recognition Using Biometric-Capsules ». Thesis, 2020.
Trouver le texte intégralPhillips, Tyler S. « Privacy-Preserving Facial Recognition Using Biometric-Capsules ». Thesis, 2020. http://hdl.handle.net/1805/22695.
Texte intégralIn recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design. In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods.
Sui, Yan. « Design and evaluation of a secure, privacy-preserving and cancelable biometric authentication : Bio-Capsule ». Thesis, 2014. http://hdl.handle.net/1805/4985.
Texte intégralA large portion of system breaches are caused by authentication failure either during the system login process or even in the post-authentication session, which is further related to the limitations associated with existing authentication approaches. Current authentication methods, whether proxy based or biometrics based, are hardly user-centric; and they either put burdens on users or endanger users' (biometric) security and privacy. In this research, we propose a biometrics based user-centric authentication approach. The main idea is to introduce a reference subject (RS) (for each system), securely fuse the user's biometrics with the RS, generate a BioCapsule (BC) (from the fused biometrics), and employ BCs for authentication. Such an approach is user-friendly, identity-bearing yet privacy-preserving, resilient, and revocable once a BC is compromised. It also supports "one-click sign on" across multiple systems by fusing the user's biometrics with a distinct RS on each system. Moreover, active and non-intrusive authentication can be automatically performed during the user's post-authentication on-line session. In this research, we also formally prove that the proposed secure fusion based BC approach is secure against various attacks and compare the new approach with existing biometrics based approaches. Extensive experiments show that the performance (i.e., authentication accuracy) of the new BC approach is comparable to existing typical biometric authentication approaches, and the new BC approach also possesses other desirable features such as diversity and revocability.
Awwad, Sari Moh'd Ismaeil. « Tracking and fine-grained activity recognition in depth videos ». Thesis, 2016. http://hdl.handle.net/10453/90070.
Texte intégralTracking and activity recognition in video are arguably two of the most active topics within the field of computer vision and pattern recognition. Historically, tracking and activity recognition have been performed over conventional video such as color or grey-level frames, either of which contains significant clues for the identification of targets. While this is often a desirable feature within the context of video surveillance, the use of video for activity recognition or for tracking in privacy-sensitive environments such as hospitals and care facilities is often perceived as intrusive. For this reason, this PhD research has focused on providing tracking and activity recognition solely from depth videos which offer a naturally privacy-preserving visual representation of the scene at hand. Depth videos can nowadays be acquired with inexpensive and highly-available commercial sensors such as Microsoft Kinect and Asus Xtion. The two main contributions of this research have been the design of a specialised tracking algorithm for tracking in depth data, and a fine-grained activity recognition approach for recognising activities in depth video. The proposed tracker is an extension of the popular Struck algorithm, an approach that leverages a structural support vector machine (SVM) for tracking. The main contributions of the proposed tracker include a dedicated depth feature based on local depth patterns, a heuristic for handling view occlusions in depth frames, and a technique for keeping the number of support vectors within a given budget, so as to limit computational costs. Conversely, the proposed fine-grained activity recognition approach leverages multi-scale depth measurements and a Fisher-consistent multi-class SVM. In addition to the novel approaches for tracking and activity recognition, in this thesis we have canvassed and developed a practical computer vision application for the detection of hand hygiene at a hospital. This application was developed in collaboration with clinical researchers from the Intensive Care Unit of Sydney’s Royal Prince Alfred Hospital. Experiments presented through the thesis confirm that the proposed approaches are effective, and either outperform the state of the art or significantly reduce the need for sensor instrumentation. The outcomes of the hand-hygiene detection were also positively received and assessed by the clinical research unit.
Livres sur le sujet "Privacy preserving recognition"
Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. New York, NY : Springer New York, 2013.
Trouver le texte intégralPathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2014.
Trouver le texte intégralChapitres de livres sur le sujet "Privacy preserving recognition"
Erkin, Zekeriya, Martin Franz, Jorge Guajardo, Stefan Katzenbeisser, Inald Lagendijk et Tomas Toft. « Privacy-Preserving Face Recognition ». Dans Privacy Enhancing Technologies, 235–53. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03168-7_14.
Texte intégralVargas Martin, Miguel, et Patrick C. K. Hung. « Privacy-Preserving Speech Recognition ». Dans Encyclopedia of Machine Learning and Data Science, 1–6. New York, NY : Springer US, 2021. http://dx.doi.org/10.1007/978-1-4899-7502-7_984-1.
Texte intégralPathak, Manas A. « Privacy-Preserving Isolated-Word Recognition ». Dans Privacy-Preserving Machine Learning for Speech Processing, 103–9. New York, NY : Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4639-2_11.
Texte intégralSadeghi, Ahmad-Reza, Thomas Schneider et Immo Wehrenberg. « Efficient Privacy-Preserving Face Recognition ». Dans Information, Security and Cryptology – ICISC 2009, 229–44. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14423-3_16.
Texte intégralKrawczyk, Bartosz, et Michal Wozniak. « Privacy Preserving Models of k-NN Algorithm ». Dans Computer Recognition Systems 4, 207–17. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_22.
Texte intégralPathak, Manas A. « Overview of Speech Recognition with Privacy ». Dans Privacy-Preserving Machine Learning for Speech Processing, 99–102. New York, NY : Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4639-2_10.
Texte intégralJin, Xin, Yan Liu, Xiaodong Li, Geng Zhao, Yingya Chen et Kui Guo. « Privacy Preserving Face Identification in the Cloud through Sparse Representation ». Dans Biometric Recognition, 160–67. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_20.
Texte intégralAbbasi, Wisam. « Privacy-Preserving Speaker Verification and Speech Recognition ». Dans Lecture Notes in Computer Science, 102–19. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25467-3_7.
Texte intégralWang, Qi, Dehua Zhou, Quanlong Guan, Yanling Li et Jimian Yang. « A Privacy-Preserving Classifier in Statistic Pattern Recognition ». Dans Cloud Computing and Security, 496–507. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00009-7_45.
Texte intégralKumawat, Sudhakar, et Hajime Nagahara. « Privacy-Preserving Action Recognition via Motion Difference Quantization ». Dans Lecture Notes in Computer Science, 518–34. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19778-9_30.
Texte intégralActes de conférences sur le sujet "Privacy preserving recognition"
You, Zhengxin, Sheng Li, Zhenxing Qian et Xinpeng Zhang. « Reversible Privacy-Preserving Recognition ». Dans 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2021. http://dx.doi.org/10.1109/icme51207.2021.9428115.
Texte intégralZou, Chengming, Ducheng Yuan, Long Lan et Haoang Chi. « Privacy-Preserving Action Recognition ». Dans ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747456.
Texte intégralRizzo, Nicholas, Ethan Sprissler, Yuan Hong et Sanjay Goel. « Privacy preserving driving style recognition ». Dans 2015 International Conference on Connected Vehicles and Expo (ICCVE). IEEE, 2015. http://dx.doi.org/10.1109/iccve.2015.42.
Texte intégralWickramaarachchi, Wiraj Udara, Yousif A. Alhaj et Asela Gunesekera. « Effective Privacy-Preserving Iris Recognition ». Dans 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2019. http://dx.doi.org/10.1109/icivc47709.2019.8981008.
Texte intégralGeppert, Marcel, Viktor Larsson, Johannes L. Schonberger et Marc Pollefeys. « Privacy Preserving Partial Localization ». Dans 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01682.
Texte intégralAloufi, Ranya, Hamed Haddadi et David Boyle. « Configurable Privacy-Preserving Automatic Speech Recognition ». Dans Interspeech 2021. ISCA : ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1783.
Texte intégralSpeciale, Pablo, Johannes L. Schonberger, Sing Bing Kang, Sudipta N. Sinha et Marc Pollefeys. « Privacy Preserving Image-Based Localization ». Dans 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00564.
Texte intégralDai, Ji, Behrouz Saghafi, Jonathan Wu, Janusz Konrad et Prakash Ishwar. « Towards privacy-preserving recognition of human activities ». Dans 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351605.
Texte intégralErgun, Ovgu Ozturk. « Privacy preserving face recognition in encrypted domain ». Dans 2014 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2014. http://dx.doi.org/10.1109/apccas.2014.7032863.
Texte intégralNakachi, Takayuki, et Hitoshi Kiya. « Privacy-preserving Pattern Recognition with Image Compression ». Dans 8th International Conference on Signal, Image Processing and Pattern Recognition. Aircc Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.100201.
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