Federated Learning for Secure and Privacy-Preserving Voice Assistant Search

  • Authors

    • Dr. Venkatesh Iyer Professor SRM Institute of Science and Technology, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2019PII6Q3L

    Published 11-05-2019

  • Federated Learning, Voice Assistants, Privacy-Preserving Techniques, Secure Voice Search, Machine Learning, Data Security, Privacy Protection, Distributed Learning, Edge Computing, Decentralized Ai

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    V. Iyer, “Federated Learning for Secure and Privacy-Preserving Voice Assistant Search”, IJMLPA, vol. 2, no. 2, pp. 01–09, Nov. 2019, doi: 10.67228/3142788X/IJMLPA-2019PII6Q3L.

  • Abstract

    With the growing adoption of voice assistants, there is an increasing concern over the privacy and security of sensitive user data, especially in voice search functionalities. Traditional centralized machine learning models in voice assistants require the transmission of large amounts of personal data to cloud servers, raising significant privacy risks. Federated Learning, a decentralized machine learning technique, offers a promising solution by allowing data to remain on the user's device, mitigating privacy concerns. This paper explores the integration of Federated Learning into voice assistant search systems to enhance privacy preservation and security. By analyzing the benefits, challenges, and potential solutions, we demonstrate how Federated Learning can protect user data during voice searches while still enabling accurate and efficient voice recognition. Additionally, the paper discusses potential future directions and innovations to further strengthen the privacy and security aspects of voice assistant technologies.

  • References

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