Manju, S.,
Punithavalli, M.,
Neural network-based ideation learning for intelligent agents:
e-brainstorming with privacy preferences,
IJCVR(5), No. 3, 2015, pp. 231-253.
DOI Link
1509
BibRef
Oneto, L.[Luca],
Ridella, S.[Sandro],
Anguita, D.[Davide],
Differential privacy and generalization: Sharper bounds with
applications,
PRL(89), No. 1, 2017, pp. 31-38.
Elsevier DOI
1704
Differential privacy
BibRef
González-Serrano, F.J.[Francisco-Javier],
Navia-Vázquez, Á.[Ángel],
Amor-Martín, A.[Adrián],
Training Support Vector Machines with privacy-protected data,
PR(72), No. 1, 2017, pp. 93-107.
Elsevier DOI
1708
Machine learning
BibRef
Yang, J.J.[Jing-Jing],
Wu, J.Z.[Jin-Zhao],
Wang, X.J.[Xiao-Jing],
Special Issue Retraction:
Convolutional neural network based on differential privacy in
exponential attenuation mode for image classification,
IET-IPR(17), No. 1, January 2023, pp. 301.
DOI Link
2301
BibRef
And:
IET-IPR(14), No. 15, 15 December 2020, pp. 3676-3681.
DOI Link
2103
Adding gaussian noise.
BibRef
Kim, Y.,
Cho, D.,
Hong, S.,
Towards Privacy-Preserving Domain Adaptation,
SPLetters(27), 2020, pp. 1675-1679.
IEEE DOI
1806
Prototypes, Reliability, Adaptation models, Feature extraction,
Data models, Data privacy, Training, Domain adaptation,
class prototypes
BibRef
Venkategowda, N.K.D.[Naveen K. D.],
Werner, S.[Stefan],
Privacy-Preserving Distributed Maximum Consensus,
SPLetters(27), 2020, pp. 1839-1843.
IEEE DOI
2011
Privacy, Nickel, Signal processing algorithms, Optimization,
Linear programming, Convex functions, Gaussian noise, ADMM,
privacy
BibRef
Alkhelaiwi, M.[Munirah],
Boulila, W.[Wadii],
Ahmad, J.[Jawad],
Koubaa, A.[Anis],
Driss, M.[Maha],
An Efficient Approach Based on Privacy-Preserving Deep Learning for
Satellite Image Classification,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link
2106
Privacy of data and results when using cloud services for deep learning.
Train on encrypted data.
BibRef
Luo, Y.[Yi],
Feng, G.R.[Guo-Rui],
Zhang, X.P.[Xin-Peng],
Hierarchical Authorization of Convolutional Neural Networks for
Multi-User,
SPLetters(28), 2021, pp. 1560-1564.
IEEE DOI
2108
Authorization, Neural networks, Training, Mathematical model,
Testing, Differential privacy, Watermarking,
rights management
BibRef
Yang, B.Y.[Bao-Yao],
Yeh, H.W.[Hao-Wei],
Harada, T.[Tatsuya],
Yuen, P.C.[Pong C.],
Model-Induced Generalization Error Bound for Information-Theoretic
Representation Learning in Source-Data-Free Unsupervised Domain
Adaptation,
IP(31), 2022, pp. 419-432.
IEEE DOI
2112
BibRef
Earlier: A2, A1, A4, A3:
SoFA: Source-data-free Feature Alignment for Unsupervised Domain
Adaptation,
WACV21(474-483)
IEEE DOI
2106
Adaptation models, Data models, Upper bound, Predictive models,
Optimization, Computational modeling, Data privacy,
representation learning.
Training, Semantics,
Predictive models, Gaussian distribution, Feature extraction
BibRef
Yeh, H.W.[Hao-Wei],
Meng, Q.[Qier],
Harada, T.[Tatsuya],
Misalignment-Free Relation Aggregation for Multi-Source-Free Domain
Adaptation,
OutDistri23(4315-4324)
IEEE DOI
2401
BibRef
Yeh, H.W.[Hao-Wei],
Westfechtel, T.[Thomas],
Huang, J.B.[Jia-Bin],
Harada, T.[Tatsuya],
Boosting Source-free Domain Adaptation via Confidence-based Subsets
Feature Alignment,
ICPR22(2857-2863)
IEEE DOI
2212
Adaptation models, Predictive models, Multitasking, Data models,
Entropy, Pattern recognition, Reliability
BibRef
Biswas, C.[Chandan],
Ganguly, D.[Debasis],
Mukherjee, P.S.[Partha Sarathi],
Bhattacharya, U.[Ujjwal],
Hou, Y.F.[Yu-Fang],
Privacy-aware supervised classification:
An informative subspace based multi-objective approach,
PR(122), 2022, pp. 108301.
Elsevier DOI
2112
Privacy preserving representation learning,
Informative subspace, Multi-objective learning,
Defence against information stealing adversarial attacks
BibRef
Bai, J.W.[Jia-Wang],
Li, Y.M.[Yi-Ming],
Li, J.W.[Jia-Wei],
Yang, X.[Xue],
Jiang, Y.[Yong],
Xia, S.T.[Shu-Tao],
Multinomial random forest,
PR(122), 2022, pp. 108331.
Elsevier DOI
2112
Random forest, Consistency, Differential privacy, Classification
BibRef
Yin, H.X.[Hong-Xu],
Mallya, A.[Arun],
Vahdat, A.[Arash],
Alvarez, J.M.[Jose M.],
Kautz, J.[Jan],
Molchanov, P.[Pavlo],
See through Gradients: Image Batch Recovery via GradInversion,
CVPR21(16332-16341)
IEEE DOI
2111
Computation for deep networks.
Training, Deep learning, Data privacy, Image matching, Estimation,
Collaborative work, Pattern recognition
BibRef
Singh, A.[Abhishek],
Chopra, A.[Ayush],
Garza, E.[Ethan],
Zhang, E.[Emily],
Vepakomma, P.[Praneeth],
Sharma, V.[Vivek],
Raskar, R.[Ramesh],
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep
neural networks,
CVPR21(12120-12130)
IEEE DOI
2111
Deep learning, Privacy, Face recognition,
Collaboration, Medical services, Benchmark testing
BibRef
Hanzlik, L.[Lucjan],
Zhang, Y.[Yang],
Grosse, K.[Kathrin],
Salem, A.[Ahmed],
Augustin, M.[Maximilian],
Backes, M.[Michael],
Fritz, M.[Mario],
MLCapsule: Guarded Offline Deployment of Machine Learning as a
Service,
TCV21(3295-3304)
IEEE DOI
2109
Data privacy, Computational modeling, Perturbation methods,
Reverse engineering, Machine learning, Intellectual property, Data models
BibRef
Zhu, F.[Fei],
Zhang, X.Y.[Xu-Yao],
Wang, C.[Chuang],
Yin, F.[Fei],
Liu, C.L.[Cheng-Lin],
Prototype Augmentation and Self-Supervision for Incremental Learning,
CVPR21(5867-5876)
IEEE DOI
2111
Training, Learning systems, Deep learning, Data privacy,
Computational modeling, Prototypes
BibRef
Choi, Y.[Yoojin],
El-Khamy, M.[Mostafa],
Lee, J.[Jungwon],
Dual-Teacher Class-Incremental Learning With Data-Free Generative
Replay,
CLVision21(3538-3547)
IEEE DOI
2109
Data privacy, Training data,
Data models, Pattern recognition, Knowledge transfer
BibRef
Reed, C.J.[Colorado J.],
Metzger, S.[Sean],
Srinivas, A.[Aravind],
Darrell, T.J.[Trevor J.],
Keutzer, K.[Kurt],
SelfAugment:
Automatic Augmentation Policies for Self-Supervised Learning,
CVPR21(2673-2682)
IEEE DOI
2111
Training, Data privacy, Correlation,
Image recognition, Network architecture, Pattern recognition
BibRef
Kurmi, V.K.[Vinod K.],
Subramanian, V.K.[Venkatesh K.],
Namboodiri, V.P.[Vinay P.],
Domain Impression: A Source Data Free Domain Adaptation Method,
WACV21(615-625)
IEEE DOI
2106
Adaptation models, Data privacy,
Analytical models, Computational modeling, Memory management
BibRef
Ahmed, S.M.[Sk Miraj],
Raychaudhuri, D.S.[Dripta S.],
Paul, S.[Sujoy],
Oymak, S.[Samet],
Roy-Chowdhury, A.K.[Amit K.],
Unsupervised Multi-source Domain Adaptation Without Access to Source
Data,
CVPR21(10098-10107)
IEEE DOI
2111
Training, Adaptation models, Data privacy,
Predictive models, Benchmark testing, Data models
BibRef
Kundu, J.N.[Jogendra Nath],
Venkat, N.[Naveen],
Revanur, A.[Ambareesh],
Rahul, M.V.,
Babu, R.V.[R. Venkatesh],
Towards Inheritable Models for Open-Set Domain Adaptation,
CVPR20(12373-12382)
IEEE DOI
2008
Adaptation models, Task analysis, Data models, Predictive models,
Computational modeling, Data privacy, Training
BibRef
Sarpatwar, K.,
Ratha, N.,
Nandakumar, K.,
Shanmugam, K.,
Rayfield, J.T.,
Pankanti, S.,
Vaculin, R.,
Privacy Enhanced Decision Tree Inference,
TCV20(154-159)
IEEE DOI
2008
Decision trees, Privacy, Machine learning, Encryption, Data models, Data privacy
BibRef
Wu, Z.Y.[Zhen-Yu],
Wang, Z.Y.[Zhang-Yang],
Wang, Z.W.[Zhao-Wen],
Jin, H.L.[Hai-Lin],
Towards Privacy-Preserving Visual Recognition via Adversarial Training:
A Pilot Study,
ECCV18(XVI: 627-645).
Springer DOI
WWW Link.
1810
See also Privacy-Preserving Visual Recognition PA-HMDB51.
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Meta-Learning .