A Differential Approach for Data and Classification Service-Based Privacy-Preserving Machine Learning Model in Cloud Environment View Full Text


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Article Info

DATE

2022-07-09

AUTHORS

Rishabh Gupta, Ashutosh Kumar Singh

ABSTRACT

The massive upsurge in computational and storage has driven the local data and machine learning applications to the cloud environment. The owners may not fully trust the cloud environment as it is managed by third parties. However, maintaining privacy while sharing data and the classifier with several stakeholders is a critical challenge. This paper proposes a novel model based on differential privacy and machine learning approaches that enable multiple owners to share their data for utilization and the classifier to render classification services for users in the cloud environment. To process owners’ data and classifier, the model specifies a communication protocol among various untrustworthy parties. The proposed model also provides a robust mechanism to preserve the privacy of data and the classifier. The experiments are conducted for a Naive Bayes classifier over numerous data sets to compute the proposed model’s efficiency. The achieved results demonstrate that the proposed model has high accuracy, precision, recall, and F1-score up to 94%, 95%, 94%, and 94%, and improvement up to 16.95%, 20.16%, 16.95%, and 23.33%, respectively, compared with state-of-the-art works. More... »

PAGES

737-764

References to SciGraph publications

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  • 2017-04-08. Privacy-preserving outsourced classification in cloud computing in CLUSTER COMPUTING
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  • 2021-07-30. A Comprehensive Approach to Android Malware Detection Using Machine Learning in INFORMATION SECURITY TECHNOLOGIES FOR CONTROLLING PANDEMICS
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  • 2020-05-08. Adaptive privacy-preserving federated learning in PEER-TO-PEER NETWORKING AND APPLICATIONS
  • 2017-01-23. Privacy in cloud computing environments: a survey and research challenges in THE JOURNAL OF SUPERCOMPUTING
  • 2020-10-22. Local Differential Privacy for Data Streams in SECURITY AND PRIVACY IN DIGITAL ECONOMY
  • 2020-03-14. BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting in SOFT COMPUTING
  • 2017-04-06. Efficient and secure BIG data delivery in Cloud Computing in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2013-09-04. Secure Data Sharing in the Cloud in SECURITY, PRIVACY AND TRUST IN CLOUD SYSTEMS
  • 2006. Calibrating Noise to Sensitivity in Private Data Analysis in THEORY OF CRYPTOGRAPHY
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  • 2021-03-22. Machine Learning: Algorithms, Real-World Applications and Research Directions in SN COMPUTER SCIENCE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00354-022-00185-z

    DOI

    http://dx.doi.org/10.1007/s00354-022-00185-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149358674


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