Privacy Preserving Federated Learning Solution for Security of Industrial Cyber Physical Systems

2021 ◽  
pp. 195-211
Author(s):  
Seyed Hossein Majidi ◽  
Hadi Asharioun
Keyword(s):  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Physical Systems

An Efficient Privacy-Preserving Authenticated Key Establishment Protocol for Health Monitoring in Industrial Cyber-Physical Systems

2021 ◽  
pp. 1-1
Author(s):  
Salman Shamshad ◽  
Khalid Mahmood ◽  
Shafiq Hussain ◽  
Sahil Garg Ashok Kumar Das ◽  
Neeraj Kumar Joel J. P. C. Rodrigues
Keyword(s):  
Health Monitoring ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Key Establishment ◽  
Physical Systems

scholarly journals Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

2021 ◽  
Author(s):  
Linlin Zhang ◽  
Zehui Zhang ◽  
Cong Guan
Keyword(s):  
Convergence Rate ◽  
Performance Improvement ◽  
Gradient Descent ◽  
Homomorphic Encryption ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption ◽  
Physical Systems ◽  
Cloud Server ◽  
And Performance

AbstractFederated learning (FL) is a distributed learning approach, which allows the distributed computing nodes to collaboratively develop a global model while keeping their data locally. However, the issues of privacy-preserving and performance improvement hinder the applications of the FL in the industrial cyber-physical systems (ICPSs). In this work, we propose a privacy-preserving momentum FL approach, named PMFL, which uses the momentum term to accelerate the model convergence rate during the training process. Furthermore, a fully homomorphic encryption scheme CKKS is adopted to encrypt the gradient parameters of the industrial agents’ models for preserving their local privacy information. In particular, the cloud server calculates the global encrypted momentum term by utilizing the encrypted gradients based on the momentum gradient descent optimization algorithm (MGD). The performance of the proposed PMFL is evaluated on two common deep learning datasets, i.e., MNIST and Fashion-MNIST. Theoretical analysis and experiment results confirm that the proposed approach can improve the convergence rate while preserving the privacy information of the industrial agents.

scholarly journals Privacy-Preserving Broker-ABE Scheme for Multiple Cloud-Assisted Cyber Physical Systems

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5463 ◽  
Author(s):  
Po-Wen Chi ◽  
Ming-Hung Wang
Keyword(s):  
Data Privacy ◽  
Service Providers ◽  
Real Life ◽  
Data Communication ◽  
Physical Space ◽  
Cloud Service ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Sensor Data ◽  
Physical Systems

Cloud-assisted cyber–physical systems (CCPSs) integrate the physical space with cloud computing. To do so, sensors on the field collect real-life data and forward it to clouds for further data analysis and decision-making. Since multiple services may be accessed at the same time, sensor data should be forwarded to different cloud service providers (CSPs). In this scenario, attribute-based encryption (ABE) is an appropriate technique for securing data communication between sensors and clouds. Each cloud has its own attributes and a broker can determine which cloud is authorized to access data by the requirements set at the time of encryption. In this paper, we propose a privacy-preserving broker-ABE scheme for multiple CCPSs (MCCPS). The ABE separates the policy embedding job from the ABE task. To ease the computational burden of the sensors, this scheme leaves the policy embedding task to the broker, which is generally more powerful than the sensors. Moreover, the proposed scheme provides a way for CSPs to protect data privacy from outside coercion.

scholarly journals Privacy-Preserving Estimation of $k$ -Persistent Traffic in Vehicular Cyber-Physical Systems

2019 ◽  
Vol 6 (5) ◽  
pp. 8296-8309 ◽  
Author(s):  
Yu-E Sun ◽  
He Huang ◽  
Shigang Chen ◽  
You Zhou ◽  
Kai Han ◽  
...  
Keyword(s):  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Physical Systems

scholarly journals Privacy-Preserving Resilience of Cyber-Physical Systems to Adversaries

2020 ◽  
Author(s):  
Bhaskar Ramasubramanian ◽  
Luyao Niu ◽  
Andrew Clark ◽  
Linda Bushnell ◽  
Radha Poovendran
Keyword(s):  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Physical Systems

scholarly journals A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems

2018 ◽  
Vol 14 (3-4) ◽  
pp. 1-22 ◽  
Author(s):  
Fisayo Caleb Sangogboye ◽  
Ruoxi Jia ◽  
Tianzhen Hong ◽  
Costas Spanos ◽  
Mikkel Baun Kjærgaard
Keyword(s):  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Physical Systems ◽  
Privacy Preserving Data Publishing
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