Relevant bibliographies by topics / Privacy preserving recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

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"

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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 (July 8, 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 (August 31, 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 regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.
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Wang, Lin, Chuan Zhao, Kun Zhao, Bo Zhang, Shan Jing, Zhenxiang Chen, and Kuiheng Sun. "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 features and labels, which raises privacy problems. We decentralize the training process locally to each client in order to ensure the privacy of training data. Furthermore, we use an advanced secure aggregation algorithm to ensure that malicious participants cannot extract private information from the updated parameters even during the aggregation phase. Edge computing nodes have been introduced into our system to address the problem of data generation devices’ insufficient computing power. We replace the traditional central server with smart contract to make the system more robust and secure. We achieve the verifiability of the packaged nodes using the publicly auditability feature of blockchain. According to the experimental data, the accuracy of the HAR model trained by our proposed framework reaches 93.24%, which meets the applicability requirements. The use of secure multiparty computation techniques unavoidably increases training time, and experimental results show that a round of iterations takes 36.4 seconds to execute, which is still acceptable.
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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 (March 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 algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.
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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 (June 28, 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 identify channels important for face image visualization but not crucial for maintaining high FR accuracy. For this purpose, the loss function of the analysis network consists of the empirical FR error loss and a face visualization penalty term, and the network is trained in an end-to-end manner. We find that with the developed analysis network, more than 94% of the image energy can be dropped while the face recognition accuracy stays almost undegraded. In order to further protect the remaining frequency components, we propose a fast masking method. Effectiveness of the new scheme in removing the visual information of face images while maintaining their distinguishability is validated over several large face datasets. Results show that the proposed scheme achieves a recognition performance and inference time comparable to ArcFace operating on original face images directly.
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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 (June 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
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 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.
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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 characteristics'' (ISO/IEC JTC1 SC37). This method of recognition has the advantage that it does not need the memorization of any password or the possess of any token, at the same time, however, biometrics cannot be changed if compromised in any way, hence calling for the adoption of suitable protection mechanisms. In this thesis we study the development of privacy preserving protocols for biometric recognition. This is a new research field for which a number of solutions have been proposed in recent years. For efficiency reasons, the majority of those solutions are secure only against a passive adversary, that is an adversary that does not deviate from the protocol, yet tries to infer as much information as possible from the data exchanged during the protocol. On the contrary, in this thesis we look for protocols which are secure against active adversaries, that is adversaries that deliberately and arbitrarily deviate from the recognition protocol. Specifically, we propose two possible solutions using signal processing in the encrypted domain's tools. First we use a cryptographic scheme belonging to the somewhat homomorphic scheme's family and we propose both an identification and an authentication non-interactive scheme. The first protocol focuses on a one-to-many recognition task: the biometric probe of a specific individual is compared with all the probes contained in a database looking for a positive match. The second protocol, instead, considers a one to one comparison. The new probe of an enrolled individual is compared with the probe of the same individual stored during the enrollment phase. As a second contribution, we propose SEMBA: a protocol secure against active adversary for multibiometric recognition. In this case we look for a trade-off between efficiency and accuracy by combining information from two biometric traits instead of only one. The protocol relies on SPDZ, a new framework proposed by Damgård et al. which is secure also in the presence of an active adversary.
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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 relatively difficult to circumvent. However, although biometrics based solutions provide various advantages, there exist some inherent concerns of the technology. In the first place, biometrics can not be easily changed or reissued if compromised due to the limited number of biometric traits that humans possess. Secondly, since biometric data reflect the user's physiological or behavioral characteristics, privacy issues arise if the stored biometric templates are obtained by an adversary. To that end, changeability and privacy protection of biometric templates are two important issues that need to be addressed for widespread deployment of biometric technology. This dissertation systematically investigates random transformation based methods for addressing the challenging problems of changeability and privacy protection in biometrics enabled recognition systems. A random projection based approach is first introduced. We present a detailed mathematical analysis on the similarity and privacy preserving properties of random projection, and introduce a vector translation technique to achieve strong changeability. To further enhance privacy protection as well as to improve the recognition accuracy, a sorted index number (SIN) approach is proposed such that only the index numbers of the sorted feature vectors are stored as templates. The SIN framework is then evaluated in conjunction with random additive transform, random multiplicative transform, and random projection, for producing reissuable and privacy preserving biometric templates. The feasibility of the introduced solutions is well supported by detailed theoretical analyses. Extensive experimentation on a face based biometric recognition problem demonstrates the effectiveness of the proposed methods.
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(8782193), Tyler Stephen Phillips. "Privacy-Preserving Facial Recognition Using Biometric-Capsules." Thesis, 2020.

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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.

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.
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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)
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. 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.
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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)
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 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.
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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.
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 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.
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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. New York, NY: 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, 235–53. Berlin, Heidelberg: 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, 1–6. New York, NY: 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, 103–9. New York, NY: 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, 229–44. Berlin, Heidelberg: 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, 207–17. Berlin, Heidelberg: 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, 99–102. New York, NY: 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, 160–67. Cham: 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, 102–19. Cham: 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, 496–507. Cham: 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, 518–34. Cham: 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: 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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