Hybrid Approach to Speed-Up the Privacy Preserving Kernel K-means Clustering and its Application in Social Distributed Environment

2020 ◽  
Vol 28 (2) ◽  
pp. 398-422
Author(s):  
P. L. Lekshmy ◽  
M. Abdul Rahiman
Keyword(s):  
Hybrid Approach ◽  
Privacy Preserving ◽  
Distributed Environment ◽  
Speed Up

scholarly journals Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 94
Author(s):  
Haokun Fang ◽  
Quan Qian
Keyword(s):  
Machine Learning ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Great Success ◽  
Learning Framework ◽  
Computational Overhead ◽  
Important Concern ◽  
Speed Up ◽  
Key Length ◽  
Core Idea

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

A hybrid approach for privacy-preserving RFID tags

2009 ◽  
Vol 31 (4) ◽  
pp. 812-815 ◽  
Author(s):  
Eun-Kyung Ryu ◽  
Tsuyoshi Takagi
Keyword(s):  
Hybrid Approach ◽  
Privacy Preserving ◽  
Rfid Tags

scholarly journals A Hybrid Approach to Privacy-Preserving Federated Learning

2019 ◽  
Vol 42 (5) ◽  
pp. 356-357 ◽  
Author(s):  
Stacey Truex ◽  
Nathalie Baracaldo ◽  
Ali Anwar ◽  
Thomas Steinke ◽  
Heiko Ludwig ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Privacy Preserving

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.

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