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  • 1. Supervised Text Classification with Leveled Homomorphic Encryption Archived
    Articles and reports: 11-522-X202100100027
    Description:

    Privacy concerns are a barrier to applying remote analytics, including machine learning, on sensitive data via the cloud. In this work, we use a leveled fully Homomorphic Encryption scheme to train an end-to-end supervised machine learning algorithm to classify texts while protecting the privacy of the input data points. We train our single-layer neural network on a large simulated dataset, providing a practical solution to a real-world multi-class text classification task. To improve both accuracy and training time, we train an ensemble of such classifiers in parallel using ciphertext packing.

    Key Words: Privacy Preservation, Machine Learning, Encryption

    Release date: 2021-10-29
Articles and reports (1)

Articles and reports (1) ((1 result))

  • 1. Supervised Text Classification with Leveled Homomorphic Encryption Archived
    Articles and reports: 11-522-X202100100027
    Description:

    Privacy concerns are a barrier to applying remote analytics, including machine learning, on sensitive data via the cloud. In this work, we use a leveled fully Homomorphic Encryption scheme to train an end-to-end supervised machine learning algorithm to classify texts while protecting the privacy of the input data points. We train our single-layer neural network on a large simulated dataset, providing a practical solution to a real-world multi-class text classification task. To improve both accuracy and training time, we train an ensemble of such classifiers in parallel using ciphertext packing.

    Key Words: Privacy Preservation, Machine Learning, Encryption

    Release date: 2021-10-29

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