scholarly journals SwaNN: Switching among Cryptographic Tools for Privacy-preserving Neural Network Predictions

2020 ◽  
Author(s):  
Gamze Tillem ◽  
Beyza Bozdemir ◽  
Melek Önen
Keyword(s):  
Neural Network ◽  
Privacy Preserving

An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference

2020 ◽  
pp. 419-439
Author(s):  
Liyan Shen ◽  
Xiaojun Chen ◽  
Jinqiao Shi ◽  
Ye Dong ◽  
Binxing Fang
Keyword(s):  
Neural Network ◽  
Network Inference ◽  
Privacy Preserving

scholarly journals Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions

2020 ◽  
pp. 1-1
Author(s):  
Minghui Li ◽  
Sherman S. M. Chow ◽  
Shengshan Hu ◽  
Yuejing Yan ◽  
Shen Chao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Privacy Preserving

Non-interactive privacy-preserving neural network prediction

2019 ◽  
Vol 481 ◽  
pp. 507-519 ◽  
Author(s):  
Xu Ma ◽  
Xiaofeng Chen ◽  
Xiaoyu Zhang
Keyword(s):  
Neural Network ◽  
Privacy Preserving ◽  
Neural Network Prediction ◽  
Network Prediction

scholarly journals SABlockFL: a blockchain-based smart agent system architecture and its application in federated learning

2020 ◽  
Vol 4 (2) ◽  
pp. 133-147
Author(s):  
Zhizhao Zhang ◽  
Tianzhi Yang ◽  
Yuan Liu
Keyword(s):  
Neural Network ◽  
System Architecture ◽  
Data Exchange ◽  
Privacy Preserving ◽  
Agent System ◽  
Content Type ◽  
New Era ◽  
Agent Model ◽  
Blockchain Technology ◽  
Smart Contract

Purpose The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques. Design/methodology/approach In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage. Findings A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design. Originality/value The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

Rule Extraction from Privacy Preserving Neural Network: Application to Banking

2011 ◽  
Vol 403-408 ◽  
pp. 920-928 ◽  
Author(s):  
Nekuri Naveen ◽  
V. Ravi ◽  
C. Raghavendra Rao
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Privacy Preservation ◽  
Cross Validation ◽  
Hybrid Approach ◽  
Rule Extraction ◽  
Privacy Preserving ◽  
Preservation Method ◽  
Network Application ◽  
Fold Cross Validation

In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.

scholarly journals Privacy Preserving Back-Propagation Neural Network with Cloud

IJARCCE ◽  
2017 ◽  
Vol 6 (5) ◽  
pp. 311-316
Author(s):  
Kalpana Vyavahare ◽  
Aniket Khobragade ◽  
Pratiksha Wankhade ◽  
Atthar Mansuri ◽  
Sampada Kulkarni
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Privacy Preserving

scholarly journals POSEIDON: Privacy-Preserving Federated Neural Network Learning

2021 ◽  
Author(s):  
Sinem Sav ◽  
Apostolos Pyrgelis ◽  
Juan Ramón Troncoso-Pastoriza ◽  
David Froelicher ◽  
Jean-Philippe Bossuat ◽  
...  
Keyword(s):  
Neural Network ◽  
Privacy Preserving ◽  
Neural Network Learning ◽  
Network Learning
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