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Relevant bibliographies by topics / Privacy preserving recognition

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Academic literature on the topic 'Privacy preserving recognition'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Privacy preserving recognition"

1

Chamikara, M. A. P., P. Bertok, I. Khalil, D. Liu, and S. Camtepe. "Privacy Preserving Face Recognition Utilizing Differential Privacy." Computers & Security 97 (October 2020): 101951. http://dx.doi.org/10.1016/j.cose.2020.101951.

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Li, Yuancheng, Yimeng Wang, and Daoxing Li. "Privacy-preserving lightweight face recognition." Neurocomputing 363 (October 2019): 212–22. http://dx.doi.org/10.1016/j.neucom.2019.07.039.

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Woubie, Abraham, and Tom Backstrom. "Federated Learning for Privacy-Preserving Speaker Recognition." IEEE Access 9 (2021): 149477–85. http://dx.doi.org/10.1109/access.2021.3124029.

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Xiang, Can, Chunming Tang, Yunlu Cai, and Qiuxia Xu. "Privacy-preserving face recognition with outsourced computation." Soft Computing 20, no. 9 (2015): 3735–44. http://dx.doi.org/10.1007/s00500-015-1759-5.

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Xu, Honghui, Zhipeng Cai, and Wei Li. "Privacy-Preserving Mechanisms for Multi-Label Image Recognition." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (2022): 1–21. http://dx.doi.org/10.1145/3491231.

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Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a

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Wang, Lin, Chuan Zhao, Kun Zhao, et al. "Privacy-Preserving Collaborative Computation for Human Activity Recognition." Security and Communication Networks 2022 (February 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/9428610.

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Human Activity Recognition (HAR) enables computer systems to assist users with their tasks and improve their quality of life in rehabilitation, daily life tracking, fitness, and cognitive disorder therapy. It is a hot topic in the field of machine learning, and HAR is gaining more attention among researchers due to its unique societal and economic advantages. This paper focuses on a collaborative computation scenario where a group of participants will securely and collaboratively train an accurate HAR model. The training process requires collecting a massive number of personal activity feature

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Kumar, Kambala Vijaya, and Jonnadula Harikiran. "Privacy preserving human activity recognition framework using an optimized prediction algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 254. http://dx.doi.org/10.11591/ijai.v11.i1.pp254-264.

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Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an

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Wang, Yinggui, Jian Liu, Man Luo, Le Yang, and Li Wang. "Privacy-Preserving Face Recognition in the Frequency Domain." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2558–66. http://dx.doi.org/10.1609/aaai.v36i3.20157.

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Some applications may require performing face recognition (FR) on third-party servers, which could be accessed by attackers with malicious intents to compromise the privacy of users’ face information. This paper advocates a practical privacy-preserving FR scheme without key management realized in the frequency domain. The new scheme first collects the components of the same frequency from different blocks of a face image to form component channels. Only part of the channels are retained and fed into the analysis network that performs an interpretable privacy-accuracy trade-off analysis to iden

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Zapechnikov, 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.

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Ma, Zhuo, Yang Liu, Ximeng Liu, Jianfeng Ma, and Kui Ren. "Lightweight Privacy-Preserving Ensemble Classification for Face Recognition." IEEE Internet of Things Journal 6, no. 3 (2019): 5778–90. http://dx.doi.org/10.1109/jiot.2019.2905555.

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Dissertations / Theses on the topic "Privacy preserving recognition"

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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.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O 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 aumen

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Giulia, Droandi. "Secure Processing of Biometric Signals in Malicious Setting." Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1061228.

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In the digital and interconnected world we live in, establishing the identity of any individual is a pressing need. Home banking, on line shopping, and social care web sites are only few examples of services where proof of identity is fundamental. Such a process can be based on "what you know" (i.g. a password), on"what you posses" (i.g. the key of a house or an ID card) or on "what you are"(ID-based, i.g. biometrics). In this thesis we focus on biometrics. Biometric recognition, or simply biometrics, refers to ``the automated recognition of individuals based on behavioral and biological ch

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Wang, Yongjin. "Changeable and Privacy Preserving Face Recognition." Thesis, 2010. http://hdl.handle.net/1807/26390.

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Traditional methods of identity recognition are based on the knowledge of a password or a PIN, or possession factors such as tokens and ID cards. Such strategies usually afford low level of security, and can not meet the requirements of applications with high security demands. Biometrics refer to the technology of recognizing or validating the identity of an individual based on his/her physiological and/or behavioral characteristics. It is superior to conventional methods in both security and convenience since biometric traits can not be lost, forgotten, or stolen as easily, and it is relative

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(8782193), Tyler Stephen Phillips. "Privacy-Preserving Facial Recognition Using Biometric-Capsules." Thesis, 2020.

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<div>In 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. </div><di

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Phillips, Tyler S. "Privacy-Preserving Facial Recognition Using Biometric-Capsules." Thesis, 2020. http://hdl.handle.net/1805/22695.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>In 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 inte

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Sui, Yan. "Design and evaluation of a secure, privacy-preserving and cancelable biometric authentication : Bio-Capsule." Thesis, 2014. http://hdl.handle.net/1805/4985.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>A 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

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Awwad, Sari Moh'd Ismaeil. "Tracking and fine-grained activity recognition in depth videos." Thesis, 2016. http://hdl.handle.net/10453/90070.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>Tracking 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

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Books on the topic "Privacy preserving recognition"

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Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2013.

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Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2014.

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Book chapters on the topic "Privacy preserving recognition"

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Erkin, Zekeriya, Martin Franz, Jorge Guajardo, Stefan Katzenbeisser, Inald Lagendijk, and Tomas Toft. "Privacy-Preserving Face Recognition." In Privacy Enhancing Technologies. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03168-7_14.

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Vargas Martin, Miguel, and Patrick C. K. Hung. "Privacy-Preserving Speech Recognition." In Encyclopedia of Machine Learning and Data Science. Springer US, 2021. http://dx.doi.org/10.1007/978-1-4899-7502-7_984-1.

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Pathak, Manas A. "Privacy-Preserving Isolated-Word Recognition." In Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4639-2_11.

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Sadeghi, Ahmad-Reza, Thomas Schneider, and Immo Wehrenberg. "Efficient Privacy-Preserving Face Recognition." In Information, Security and Cryptology – ICISC 2009. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14423-3_16.

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Krawczyk, Bartosz, and Michal Wozniak. "Privacy Preserving Models of k-NN Algorithm." In Computer Recognition Systems 4. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_22.

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Pathak, Manas A. "Overview of Speech Recognition with Privacy." In Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4639-2_10.

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Jin, Xin, Yan Liu, Xiaodong Li, Geng Zhao, Yingya Chen, and Kui Guo. "Privacy Preserving Face Identification in the Cloud through Sparse Representation." In Biometric Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_20.

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Abbasi, Wisam. "Privacy-Preserving Speaker Verification and Speech Recognition." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25467-3_7.

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Wang, Qi, Dehua Zhou, Quanlong Guan, Yanling Li, and Jimian Yang. "A Privacy-Preserving Classifier in Statistic Pattern Recognition." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00009-7_45.

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Kumawat, Sudhakar, and Hajime Nagahara. "Privacy-Preserving Action Recognition via Motion Difference Quantization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19778-9_30.

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Conference papers on the topic "Privacy preserving recognition"

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You, Zhengxin, Sheng Li, Zhenxing Qian, and Xinpeng Zhang. "Reversible Privacy-Preserving Recognition." In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2021. http://dx.doi.org/10.1109/icme51207.2021.9428115.

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Zou, Chengming, Ducheng Yuan, Long Lan, and Haoang Chi. "Privacy-Preserving Action Recognition." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747456.

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Rizzo, Nicholas, Ethan Sprissler, Yuan Hong, and Sanjay Goel. "Privacy preserving driving style recognition." In 2015 International Conference on Connected Vehicles and Expo (ICCVE). IEEE, 2015. http://dx.doi.org/10.1109/iccve.2015.42.

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Wickramaarachchi, Wiraj Udara, Yousif A. Alhaj, and Asela Gunesekera. "Effective Privacy-Preserving Iris Recognition." In 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2019. http://dx.doi.org/10.1109/icivc47709.2019.8981008.

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Geppert, Marcel, Viktor Larsson, Johannes L. Schonberger, and Marc Pollefeys. "Privacy Preserving Partial Localization." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01682.

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Aloufi, Ranya, Hamed Haddadi, and David Boyle. "Configurable Privacy-Preserving Automatic Speech Recognition." In Interspeech 2021. ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1783.

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Speciale, Pablo, Johannes L. Schonberger, Sing Bing Kang, Sudipta N. Sinha, and Marc Pollefeys. "Privacy Preserving Image-Based Localization." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00564.

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Dai, Ji, Behrouz Saghafi, Jonathan Wu, Janusz Konrad, and Prakash Ishwar. "Towards privacy-preserving recognition of human activities." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351605.

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Ergun, Ovgu Ozturk. "Privacy preserving face recognition in encrypted domain." In 2014 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2014. http://dx.doi.org/10.1109/apccas.2014.7032863.

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Nakachi, Takayuki, and Hitoshi Kiya. "Privacy-preserving Pattern Recognition with Image Compression." In 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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