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Privacy-Preserving and Computational Efficient Cloud-Assisted Mobile Computing System for Deep Learning

https://doi.org/10.2139/ssrn.4706046
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-96878-0_17openalex-extracted
Fast Homomorphic Evaluation of Deep Discretized Neural Networks
inferredcites →https://doi.org/10.1007/978-3-658-40442-0_9openalex-extracted
Generative Adversarial Nets
inferredcites →https://doi.org/10.1007/s10479-005-5724-zopenalex-extracted
A Tutorial on the Cross-Entropy Method
inferredcites →https://doi.org/10.1016/j.sysarc.2020.101954openalex-extracted
TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning
inferredcites →https://doi.org/10.1109/acssc.2003.1292216openalex-extracted
Multiscale structural similarity for image quality assessment
inferredcites →https://doi.org/10.1109/comst.2018.2844341openalex-extracted
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-extracted
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.522openalex-extracted
Inverting Visual Representations with Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-extracted
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00958openalex-extracted
NISP: Pruning Networks Using Neuron Importance Score Propagation
inferredcites →https://doi.org/10.1109/cvpr.2019.00289openalex-extracted
Variational Convolutional Neural Network Pruning
inferredcites →https://doi.org/10.1109/cvpr.2019.00290openalex-extracted
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00447openalex-extracted
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
inferredcites →https://doi.org/10.1109/icce-asia.2016.7804738openalex-extracted
An efficient pruning and weight sharing method for neural network
inferredcites →https://doi.org/10.1109/iccv.2017.155openalex-extracted
Channel Pruning for Accelerating Very Deep Neural Networks
inferredcites →https://doi.org/10.1109/infocom.2018.8485905openalex-extracted
DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
inferredcites →https://doi.org/10.1109/infocom.2018.8486406openalex-extracted
A Computing Platform for Video Crowdprocessing Using Deep Learning
inferredcites →https://doi.org/10.1109/jiot.2021.3060764openalex-extracted
Privacy-Preserving Machine Learning Training in IoT Aggregation Scenarios
inferredcites →https://doi.org/10.1109/tc.2015.2470255openalex-extracted
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning
inferredcites →https://doi.org/10.1109/tifs.2017.2787987openalex-extracted
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
inferredcites →https://doi.org/10.1109/tip.2007.901820openalex-extracted
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
inferredcites →https://doi.org/10.1109/tkde.2018.2878698openalex-extracted
Notice of Removal: Deep Private-Feature Extraction
inferredcites →https://doi.org/10.1109/tpds.2016.2624733openalex-extracted
AppBooster: Boosting the Performance of Interactive Mobile Applications with Computation Offloading and Parameter Tuning
inferredcites →https://doi.org/10.1109/tsc.2018.2868750openalex-extracted
PDLM: Privacy-Preserving Deep Learning Model on Cloud with Multiple Keys
inferredcites →https://doi.org/10.1145/2699343.2699349openalex-extracted
Can Deep Learning Revolutionize Mobile Sensing?
inferredcites →https://doi.org/10.1145/2810103.2813687openalex-extracted
Privacy-Preserving Deep Learning
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-extracted
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3123939.3124552openalex-extracted
C ir CNN
inferredcites →https://doi.org/10.1145/3133956.3134056openalex-extracted
Oblivious Neural Network Predictions via MiniONN Transformations
inferredcites →https://doi.org/10.1561/2200000001openalex-extracted
Graphical Models, Exponential Families, and Variational Inference
inferredcites →https://doi.org/10.1561/9781601981851openalex-extracted
Graphical Models, Exponential Families, and Variational Inference
inferredcites →https://doi.org/10.3390/s22062112openalex-extracted
A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things
inferredcites →https://doi.org/10.4230/lipics.ecrts.2024.13openalex-extracted
DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-extracted
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1506.02557openalex-extracted
Variational Dropout and the Local Reparameterization Trick
inferredcites →https://doi.org/10.48550/arxiv.1510.00149openalex-extracted
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
inferredcites →https://doi.org/10.48550/arxiv.1605.08695openalex-extracted
TensorFlow: A system for large-scale machine learning
inferredcites →https://doi.org/10.48550/arxiv.1701.05369openalex-extracted
Variational Dropout Sparsifies Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1809.03428openalex-extracted
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
inferredcites →https://doi.org/10.48550/arxiv.1901.07827openalex-extracted
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
inferredcites →https://doi.org/10.48550/arxiv.2011.11819openalex-extracted
When Machine Learning Meets Privacy: A Survey and Outlook
inferredcites →https://doi.org/10.48550/arxiv.2104.06824openalex-extracted
Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption
inferredcites →https://doi.org/10.5281/zenodo.2594562openalex-extracted
Fast Homomorphic Evaluation of Deep Discretized Neural Networks
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