Advances in applying soft computing techniques for big data and cloud computing

[...]traditional machine learning and soft computing techniques cannot be used to classify encrypted data directly, and there are still many challenges that need to be addressed such as in machine learning and soft computing algorithms, learn ability, multi-party computation, privacy preserving, out...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2018-12, Vol.22 (23), p.7679-7683
Main Authors: Gupta, B. B., Agrawal, Dharma P., Yamaguchi, Shingo, Sheng, Michael
Format: Article
Language:English
Subjects:
Access control
Algorithms
Artificial Intelligence
Big Data
Cloud computing
Computational Intelligence
Control
Cybersecurity
Data storage
Datasets
Editorial
Engineering
Feedback
Genetic algorithms
Leakage
Localization
Machine learning
Mathematical Logic and Foundations
Mechatronics
New technology
Optimization
Parameter estimation
Privacy
R&D
Research & development
Robotics
Soft computing
Training
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Summary:[...]traditional machine learning and soft computing techniques cannot be used to classify encrypted data directly, and there are still many challenges that need to be addressed such as in machine learning and soft computing algorithms, learn ability, multi-party computation, privacy preserving, outsourcing computation and computing security. [...]as in every new technology, some challenges face the vision of the big data and cloud computing, which are the administrative policies and security concerns (i.e., secure data storage, secure computation, network security, data privacy, usage privacy, location privacy; Gupta et al. 2016, 2017a, 2018; Jain and Gupta 2016). [...]the retrieval scheme supports fine-grained-cum-flexible access control along with “Query-Response” mechanism to enable users to efficiently retrieve desired data from cloud servers. [...]the leakage resilience analysis indicates that the leakage ratio in the proposed scheme can reach roughly 1/3 and is higher than other schemes. In the SFSDE algorithm, in the analysis of the fitness landscape features of fitness distance correlation, a self-feedback operation is used to iteratively select and evaluate the mutation operators of the new SFSDE algorithm. [...]mixed strategies and self-feedback transfer are combined to design a more efficient DE algorithm and enhance the search range, convergence rate and solution accuracy.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3575-1