Cited By
View all- Cheng RJoe-Wong CVarvello M(2024)Towards Network-friendly and Privacy-preserving Immersive ComputingProceedings of the CoNEXT on Student Workshop 202410.1145/3694812.3699920(3-4)Online publication date: 9-Dec-2024
MetaFL: Privacy-preserving User Authentication in Virtual Reality with Federated Learning
Authors: 
Ruizhi Cheng, Yuetong Wu, Ashish Kundu, Hugo Latapie, Myungjin Lee, Songqing Chen, Bo HanAuthors Info & Claims
Pages 54 - 67
Abstract
The increasing popularity of virtual reality (VR) has stressed the importance of authenticating VR users while preserving their privacy. Behavioral biometrics, owing to their robustness and ease of collection, compared to traditional modes such as passwords, have become a favored authentication choice. While current approaches that utilize behavioral biometrics to train classifiers for authentication yield promising accuracy, they cause privacy breaches by sharing sensitive data with a server to train a central model. In this paper, we present MetaFL, a first-of-its-kind privacy-preserving VR authentication framework that leverages federated learning (FL) on multi-modal motion data. The design of MetaFL is motivated by our key insight that various modalities of motion data uniquely affect authentication performance for individual users and among different users. It is attributed to the fundamental challenge of privacy-preserving user authentication: users can access only their own data with limited global knowledge. To tackle this issue, MetaFL judiciously selects the most suitable modalities for each user, which is decomposed into within-user ordering and between-user selection to eliminate the complex interplay between various conflicting factors. Moreover, we develop a personalized strategy to initialize FL models, further improving authentication accuracy. Our extensive performance evaluation on six public datasets shows that MetaFL outperforms state-of-the-art FL-based models (e.g., 17--28% higher authentication accuracy), and its accuracy gap with the non-privacy-preserving central model is small (i.e., only <2%).
References
[1]
M. Agarwal, M. Mehra, R. Pawar, and D. Shah. Secure Authentication Using Dynamic Virtual Keyboard Layout. In Proceedings of International Conference & Workshop on Emerging Trends in Technology, 2011.
[2]
D. Aggarwal, J. Zhou, and A. K. Jain. FedFace: Collaborative Learning of Face Recognition Model. In Proceedings of IEEE International Joint Conference on Biometrics (IJCB), 2021.
[3]
A. Al Arafat, Z. Guo, and A. Awad. VR-Spy: A Side-Channel Attack on Virtual Key-Logging in VR Headsets. In IEEE Virtual Reality and 3D User Interfaces (VR), 2021.
[4]
A. Alzubaidi and J. Kalita. Authentication of Smartphone Users Using Behavioral Biometrics. IEEE Communications Surveys & Tutorials, 18(3):1998--2026, 2016.
[5]
Apple. About Face ID advanced technology. https://support.apple.com/en-us/HT208108, 2024. [accessed on 10/07/2024].
[6]
K. O. Bailey, J. S. Okolica, and G. L. Peterson. User Identification and Authentication Using Multi-modal Behavioral Biometrics. Computers & Security, 43:77--89, 2014.
[7]
S. L. Bangare, A. Dubal, P. S. Bangare, and S. Patil. Reviewing Otsu's Method for Image Thresholding. International Journal of Applied Engineering Research, 10(9):21777--21783, 2015.
[8]
M. Barni, G. Droandi, R. Lazzeretti, and T. Pignata. SEMBA: Secure Multi-biometric Authentication. IET Biometrics, 8(6):411--421, 2019.
[9]
V. N. Boddeti. Secure Face Matching Using Fully Homomorphic Encryption. In Proceedings of IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS), 2018.
[10]
P. Bojanowski and A. Joulin. Unsupervised Learning by Predicting Noise. In International Conference on Machine Learning (ICML), 2017.
[11]
J. Bonneau, C. Herley, P. C. Van Oorschot, and F. Stajano. The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes. In Proceedings of IEEE Symposium on Security and Privacy (S&P), 2012.
[12]
R. C. Bose and D. K. Ray-Chaudhuri. On a Class of Error Correcting Binary Group Codes. Information and Control, 3(1):68--79, 1960.
[13]
L. Bottou. Large-scale Machine Learning with Stochastic Gradient Descent. In Proceedings of International Conference on Computational Statistics, 2010.
[14]
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. LOF: Identifying Density-based Local Tutliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 93--104, 2000.
[15]
P. R. Center. How Americans View Data Privacy. https://www.pewresearch.org/internet/2023/10/18/how-americans-view-data-privacy/, 2023. [accessed on 10/07/2024].
[16]
Y. Chen, Z. Yang, R. Abbou, P. Lopes, B. Y. Zhao, and H. Zheng. User Authentication via Electrical Muscle Stimulation. In Proceedings of ACM Conference on Human Factors in Computing Systems (CHI), 2021.
[17]
K. Cheng, J. F. Tian, T. Kohno, and F. Roesner. Exploring User Reactions and Mental Models Towards Perceptual Manipulation Attacks in Mixed Reality. In Proceedings of the USENIX Security Symposium, 2023.
[18]
R. Cheng, S. Chen, and B. Han. Toward Zero-trust Security for the Metaverse. IEEE Communications Magazine, 62(2):156--162, 2023.
[19]
D. Comaniciu and P. Meer. Mean Shift Analysis and Applications. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 1999.
[20]
Y. Deng, W. Chen, J. Ren, F. Lyu, Y. Liu, Y. Liu, and Y. Zhang. TailorFL: Dual-Personalized Federated Learning under System and Data Heterogeneity. In Proceedings of ACM SenSys, 2022.
[21]
D. K. Dennis, T. Li, and V. Smith. Heterogeneity for the Win: One-Shot Federated Clustering. In Proceddings of ICML, 2021.
[22]
H. Duan, J. Li, S. Fan, Z. Lin, X. Wu, and W. Cai. Metaverse for Social Good: A University Campus Prototype. In Proceedings of ACM International Conference on Multimedia (MM), 2021.
[23]
A. Ferlini, D. Ma, R. Harle, and C. Mascolo. EarGate: Gait-based User Identification with In-ear Microphones. In Proceedings of ACM MobiCom, 2021.
[24]
M. Fredrikson, S. Jha, and T. Ristenpart. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. In Proceedings of ACM SIGSAC Conference on Computer and Communications Security (CCS), 2015.
[25]
GadgetVersus. Qualcomm Adreno 650 vs Nvidia Jetson Xavier NX GPU. https://bit.ly/42srjGz, 2023. [accessed on 10/07/2024].
[26]
Y. Gao, W. Wang, V. V. Phoha, W. Sun, and Z. Jin. EarEcho: Using Ear Canal Echo for Wearable Authentication. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3):1--24, 2019.
[27]
Y. A. Ghassabeh. A Sufficient Condition for the Convergence of the Mean Shift Algorithm with Gaussian Kernel. Journal of Multivariate Analysis, 135:1--10, 2015.
[28]
A. Ghosh, J. Chung, D. Yin, and K. Ramchandran. An Efficient Framework for Clustered Federated Learning. In Proceddings of Advances in Neural Information Processing Systems (NeurIPS), 2020.
[29]
A. Ghosh, J. Chung, D. Yin, and K. Ramchandran. An Efficient Framework for Clustered Federated Learning. IEEE Transactions on Information Theory, 68(12):8076--8091, 2022.
[30]
M. Golla, G. Ho, M. Lohmus, M. Pulluri, and E. M. Redmiles. Driving 2FA Adoption at Scale: Optimizing Two-Factor Authentication Notification Design Patterns. In Proceedings of the USENIX Security Symposium, 2021.
[31]
Google. Learn how Google improves speech models. https://support.google.com/assistant/answer/11140942?hl=en#zippy=%2Cfederated-learning, 2024. [accessed on 10/07/2024].
[32]
S. D. Gordon, D. Starin, and A. Yerukhimovich. The More The Merrier: Reducing the Cost of Large Scale MPC. In Proceedings of Annual International Conference on the Theory and Applications of Cryptographic Techniques (Eurocrypt), 2021.
[33]
N. Görnitz, A. Porbadnigk, A. Binder, C. Sannelli, M. Braun, K.-R. Müller, and M. Kloft. Learning and Evaluation in Presence of Non-iid Label Noise. In Artificial Intelligence and Statistics (AISTATS), 2014.
[34]
F. Granqvist, M. Seigel, R. Van Dalen, A. Cahill, S. Shum, and M. Paulik. Improving On-Device Speaker Verification Using Federated Learning with Privacy. In Proceddings of Interspeech, 2020.
[35]
H. Habib, P. E. Naeini, S. Devlin, M. Oates, C. Swoopes, L. Bauer, N. Christin, and L. F. Cranor. User Behaviors and Attitudes Under Password Expiration Policies. In Proceedings of Symposium on Usable Privacy and Security (SOUPS), 2018.
[36]
J. F. Hair Jr, W. C. Black, B. J. Babin, and R. E. Anderson. Multivariate Data Analysis. 2010.
[37]
A. Halimi, S. R. Kadhe, A. Rawat, and N. B. Angel. Federated Unlearning: How to Efficiently Erase a Client in FL? In Proceddings of ICML, 2022.
[38]
H. Hosseini, H. Park, S. Yun, C. Louizos, J. Soriaga, and M. Welling. Federated Learning of User Verification Models Without Sharing Embeddings. In Proceedings of ICML, 2021.
[39]
J. Hu, A. Iosifescu, and R. LiKamWa. Lenscap: Split-process Framework for Fine-Grained Visual Privacy Control for Augmented Reality Apps. In Proceedings of ACM MobiSys, 2021.
[40]
Y. Huang, S. Gupta, Z. Song, K. Li, and S. Arora. Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. In Proceedings of Conference on Neural Information Processing Systems (NIPS), 2021.
[41]
A. Jain, R. Bolle, and S. Pankanti. Introduction to Biometrics. Springer, 1996.
[42]
A. K. Jain and K. Nandakumar. Biometric Authentication: System Security and User Privacy. Computer, 45(11):87--92, 2012.
[43]
A. K. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):4--20, 2004.
[44]
I. T. Jolliffe and J. Cadima. Principal Component Analysis: a Review and Recent Developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065):20150202, 2016.
[45]
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al. Advances and Open Problems in Federated Learning. Foundations and Trends® in Machine Learning, 14(1--2):1--210, 2021.
[46]
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6):84--90, 2017.
[47]
F. Lai, X. Zhu, H. V. Madhyastha, and M. Chowdhury. Oort: Efficient Federated Learning via Guided Participant Selection. In Proceedings of USENIX OSDI, 2021.
[48]
A. Li, J. Sun, P. Li, Y. Pu, H. Li, and Y. Chen. Hermes: an Efficient Federated Learning Framework for Heterogeneous Mobile Clients. In Proceedings of ACM MobiCom, 2021.
[49]
A. Li, J. Sun, X. Zeng, M. Zhang, H. Li, and Y. Chen. Fedmask: Joint Computation and Communication-efficient Personalized Federated Learning via heterogeneous Masking. In Proceedings of ACM Sensys, 2021.
[50]
C. Li, X. Zeng, M. Zhang, and Z. Cao. PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning. In Proceedings of ACM MobiCom, 2022.
[51]
S. Li, A. Ashok, Y. Zhang, C. Xu, J. Lindqvist, and M. Gruteser. Whose Move is it Anyway? Authenticating Smart Wearable Devices Using Unique Head Movement Patterns. In Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom), 2016.
[52]
S. Li, S. Savaliya, L. Marino, A. M. Leider, and C. C. Tappert. Brain Signal Authentication for Human-Computer Interaction in Virtual Reality. In IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2019.
[53]
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3):50--60, 2020.
[54]
X. Li, R. Dowsley, and M. De Cock. Privacy-preserving Feature Selection with Secure Multiparty Computation. In Proceedings of ICML, 2021.
[55]
Y. Li and Y. Yuan. Convergence Analysis of Two-layer Neural Networks with Relu Activation. In Proceedings of Conference on Neural Information Processing Systems (NIPS), 2017.
[56]
J. Liebers, M. Abdelaziz, L. Mecke, A. Saad, J. Auda, U. Gruenefeld, F. Alt, and S. Schneegass. Understanding User Identification in Virtual Reality Through Behavioral Biometrics and the Effect of Body Normalization. In Proceedings of ACM Conference on Human Factors in Computing Systems (CHI), 2021.
[57]
J. Liebers, P. Horn, C. Burschik, U. Gruenefeld, and S. Schneegass. Using Gaze Behavior and Head Orientation for Implicit Identification in Virtual Reality. In Proceedings of the ACM Symposium on Virtual Reality Software and Technology (VRST), 2021.
[58]
F. Lin, K. W. Cho, C. Song, W. Xu, and Z. Jin. Brain Password: A Secure and Truly Cancelable Brain Biometrics for Smart Headwear. In Proceedings of ACM MobiSys, 2018.
[59]
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song. Sphereface: Deep Hypersphere Embedding for Face Recognition. In Proceedings of IEEE/CVF CVPR, 2017.
[60]
W. Liu, Y. Wen, Z. Yu, and M.Yang. Large-margin Softmax Loss for Convolutional Neural Networks. In Proceedings of ICML, 2016.
[61]
M. Luo, F. Chen, D. Hu, Y. Zhang, J. Liang, and J. Feng. No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-iid Data. In Proceedings of Conference on Neural Information Processing Systems (NIPS), 2021.
[62]
S. Luo, A. Nguyen, C. Song, F. Lin, W. Xu, and Z. Yan. OcuLock: Exploring Human Visual System for Authentication in Virtual Reality Head-mounted Display. In Proceedings of Network and Distributed System Security Symposium (NDSS), 2020.
[63]
F. Mathis, H. I. Fawaz, and M. Khamis. Knowledge-driven Biometric Authentication in Virtual Reality. In Proceedings of ACM CHI, 2020.
[64]
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
[65]
R. Miller, N. K. Banerjee, and S. Banerjee. Using Siamese Neural Networks to Perform Cross-System Behavioral Authentication in Virtual Reality. In Proceedings of IEEE Virtual Reality and 3D User Interfaces (VR), 2021.
[66]
R. Miller, N. K. Banerjee, and S. Banerjee. Combining Real-World Constraints on User Behavior with Deep Neural Networks for Virtual Reality (VR) Biometrics. In Proceedings of IEEE VR, 2022.
[67]
R. Miller, N. K. Banerjee, and S. Banerjee. Temporal Effects in Motion Behavior for Virtual Reality (VR) Biometrics. In Proceedings of IEEE VR, 2022.
[68]
F. Mo, H. Haddadi, K. Katevas, E. Marin, D. Perino, and N. Kourtellis. PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments. In Proceedings of ACM Mobisys, 2021.
[69]
G. Munilla Garrido, V. Nair, and D. Song. SoK: Data Privacy in Virtual Reality. https://arxiv.org/pdf/2301.05940.pdf, 2023. [accessed on 10/07/2024].
[70]
T. Mustafa, R. Matovu, A. Serwadda, and N. Muirhead. Unsure How to Authenticate on Your VR Headset? Come on, Use Your Head! In ACM International Workshop on Security and Privacy Analytics, 2018.
[71]
C. Niu, F. Wu, S. Tang, L. Hua, R. Jia, C. Lv, Z. Wu, and G. Chen. Billion-Scale Federated Learning on Mobile Clients: A Submodel Design with Tunable Privacy. In Proceedings of ACM MobiCom, 2020.
[72]
A. Orvieto, J. Kohler, D. Pavllo, T. Hofmann, and A. Lucchi. Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. https://arxiv.org/pdf/2106.03763.pdf, 2021.
[73]
X. Ouyang, X. Shuai, Y. Li, L. Pan, X. Zhang, H. Fu, X. Wang, S. Cao, J. Xin, H. Mok, et al. ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease. In Proceddings of ACM MobiCom, 2024.
[74]
X. Ouyang, Z. Xie, H. Fu, S. Cheng, L. Pan, N. Ling, G. Xing, J. Zhou, and J. Huang. Harmony: Heterogeneous Multi-Modal Federated Learning through Disentangled Model Training. In Proceedings of ACM MobiCom, 2023.
[75]
X. Ouyang, Z. Xie, J. Zhou, J. Huang, and G. Xing. ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition. In Proceedings of ACM Mobisys, 2021.
[76]
J. Pareek and J. Jacob. Data Compression and Visualization Using PCA and T-SNE. In Proceedings of Advances in Information Communication Technology and Computing, 2021.
[77]
H. Park, H. Hosseini, and S. Yun. Federated Learning with Metric Loss. In Proceedings of International Workshop on Federated Learning for User Privacy and Data Confidentiality, 2021.
[78]
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An Imperative Style, High-performance Deep Learning Library. In Proceedings of Conference on Neural Information Processing Systems (NIPS), 2019.
[79]
V. M. Patel, N. K. Ratha, and R. Chellappa. Cancelable Biometrics: A Review. IEEE Signal Processing Magazine, 32(5):54--65, 2015.
[80]
K. Pfeuffer, M. J. Geiger, S. Prange, L. Mecke, D. Buschek, and F. Alt. Behavioural Biometrics in VR: Identifying People from Body Motion and Relations in Virtual Reality. In Proceedings of ACM CHI, 2019.
[81]
Qualcomm. Enabling on-device learning at scale. https://www.qualcomm.com/news/onq/2021/10/enabling-device-learning-scale, 2021. [accessed on 10/07/2024].
[82]
T. Sattler, B. Leibe, and L. Kobbelt. Fast Image-based Localization Using Direct 2d-to-3d Matching. In Proceedings of IEEE/CVF ICCV, 2011.
[83]
S. Schneegass, Y. Oualil, and A. Bulling. SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull. In Proceedings of ACM CHI, 2016.
[84]
Y. Shen, H. Wen, C. Luo, W. Xu, T. Zhang, W. Hu, and D. Rus. GaitLock: Protect Virtual and Augmented Reality Headsets Using Gait. IEEE Transactions on Dependable and Secure Computing, 16(3):484--497, 2018.
[85]
C. Shi, X. Xu, T. Zhang, P. Walker, Y. Wu, J. Liu, N. Saxena, Y. Chen, and J. Yu. Face-Mic: Inferring Live Speech and Speaker Identity via Subtle Facial Dynamics Captured by AR/VR Motion Sensors. In Proceedings of ACM MobiCom, 2021.
[86]
J. Shin, Y. Li, Y. Liu, and S.-J. Lee. FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients. In Proceedings of ACM MobiSys, 2022.
[87]
S. Stephenson, B. Pal, S. Fan, E. Fernandes, Y. Zhao, and R. Chatterjee. SoK: Authentication in Augmented and Virtual Reality. In Proceedings of IEEE Symposium on Security and Privacy (S&P), 2022.
[88]
Z. Su, K. Cai, R. Beeler, L. Dresel, A. Garcia, I. Grishchenko, Y. Tian, C. Kruegel, and G. Vigna. Remote Keylogging Attacks in Multi-user VR Applications. In Proceedings of USENIX Security Symposium, 2024.
[89]
S. Sugrim, C. Liu, M. McLean, and J. Lindqvist. Robust Performance Metrics for Authentication Systems. In Proceddings of Network and Distributed Systems Security Symposium (NDSS), 2019.
[90]
J. Sun, A. Li, L. Duan, S. Alam, X. Deng, X. Guo, H. Wang, M. Gorlatova, M. Zhang, H. Li, et al. FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices. In Proceedings of ACM Sensys, 2022.
[91]
K. Sundararajan and D. L. Woodard. Deep Learning for Biometrics: A Survey. ACM Computing Surveys (CSUR), 51(3):1--34, 2018.
[92]
Tech Times. Meta's Oculus Quest 2 is the Top Selling VR Headset of 2021. https://bit.ly/3Ltruuq, 2022. [accessed on 10/07/2024].
[93]
L. Tu, X. Ouyang, J. Zhou, Y. He, and G. Xing. FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition. In Proceedings of ACM SenSys, 2021.
[94]
L. Van der Maaten and G. Hinton. Visualizing Data Using t-SNE. Journal of machine learning research, 9(11), 2008.
[95]
R. Wang, L. Huang, and C. Wang. Low-effort VR Headset User Authentication Using Head-reverberated Sounds with Replay Resistance. In Proceedings of IEEE Symposium on Security and Privacy (S&P), 2023.
[96]
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni. Generalizing From a Few Examples: A Survey on Few-shot Learning. ACM Computing Surveys (CSUR), 53(3):1--34, 2020.
[97]
J. Wu, Q. Liu, Z. Huang, Y. Ning, H. Wang, E. Chen, J. Yi, and B. Zhou. Hierarchical Personalized Federated Learning for User Modeling. In Proceedings of ACM Web Conference (WWW), 2021.
[98]
N. Wu, R. Cheng, S. Chen, and B. Han. Preserving Privacy in Mobile Spatial Computing. In Proceedings of the Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV)., 2022.
[99]
Y. Wu, C. Shi, T. Zhang, P. Walker, J. Liu, N. Saxena, and Y. Chen. Privacy Leakage via Unrestricted Motion-Position Sensors in the Age of Virtual Reality: A Study of Snooping Typed Input on Virtual Keyboards. In Proceedings of IEEE Symposium on Security and Privacy (S&P), 2023.
[100]
Q. Yang, Y. Liu, T. Chen, and Y. Tong. Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1--19, 2019.
[101]
J. Yi, S. Choi, and Y. Lee. EagleEye: Wearable Camera-based Person Identification in Crowded Urban Spaces. In Proceedings of ACM MobiCom, 2020.
[102]
X. Yin, Y. Zhu, and J. Hu. A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future directions. ACM Computing Surveys (CSUR), 54(6):1--36, 2021.
[103]
F. Yu, A. S. Rawat, A. Menon, and S. Kumar. Federated Learning with Only Positive Labels. In Proceedings of ICML, 2020.
[104]
T. Zhang, Q. Ji, Z. Ye, M. M. R. R. Akanda, A. T. Mahdad, C. Shi, Y. Wang, N. Saxena, and Y. Chen. SAFARI: Speech-Associated Facial Authentication for AR/VR Settings via Robust Vibration Signatures. In Proceedings of ACM SIGSAC Conference on Computer and Communications Security (CCS), 2024.
[105]
T. Zhang, Z. Ye, A. T. Mahdad, M. M. R. R. Akanda, C. Shi, Y. Wang, N. Saxena, and Y. Chen. FaceReader: Unobtrusively Mining Vital Signs and Vital Sign Embedded Sensitive Info via AR/VR Motion Sensors. In Proceedings of ACM SIGSAC Conference on Computer and Communications Security (CCS), 2023.
[106]
Y. Zhang, W. Hu, W. Xu, C. T. Chou, and J. Hu. Continuous Authentication Using Eye Movement Response of Implicit Visual Stimuli. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 1(4):1--22, 2018.
[107]
H. Zhu, W. Jin, M. Xiao, S. Murali, and M. Li. Blinkey: A Two-Factor User Authentication Method for Virtual Reality Devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(4):1--29, 2020.
[108]
H. Zhu, M. Xiao, and M. Li. SoundLock:A Novel User Authentication Scheme for VR Devices Using Auditory-Pupillary Response. In Proceedings of Network and Distributed System Security Symposium (NDSS), 2023.
[109]
H. Zhu, J. Xu, S. Liu, and Y. Jin. Federated Learning on non-IID data: A Survey. Neurocomputing, 465:371--390, 2021.
Index Terms
- MetaFL: Privacy-preserving User Authentication in Virtual Reality with Federated Learning
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November 2024
950 pages
ISBN:9798400706974
DOI:10.1145/3666025
- Chair:
- Jie Liu,
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View all- Cheng RJoe-Wong CVarvello M(2024)Towards Network-friendly and Privacy-preserving Immersive ComputingProceedings of the CoNEXT on Student Workshop 202410.1145/3694812.3699920(3-4)Online publication date: 9-Dec-2024
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