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Related Experiment Video

Updated: Oct 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

752

Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies.

Mert Al, Semih Yagli, Sun-Yuan Kung

    IEEE Transactions on Neural Networks and Learning Systems
    |September 16, 2021
    PubMed
    Summary

    New machine learning methods protect user privacy by removing sensitive data before sharing, maintaining data utility while preventing misuse. These techniques enable on-device data desensitization with low computational cost.

    Related Experiment Videos

    Last Updated: Oct 20, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    752

    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Privacy

    Background:

    • The proliferation of the Internet of Things (IoT) and Big Data has led to numerous data-driven applications, improving quality of life.
    • However, extensive data collection raises significant privacy concerns, necessitating methods for user control and data protection.
    • Existing methods often require complex computations or fail to balance privacy with data utility.

    Purpose of the Study:

    • To develop novel supervised and adversarial learning techniques for privacy-preserving data desensitization.
    • To enable simultaneous optimization of privacy-preserving feature mappings and predictive models.
    • To ensure computational efficiency for on-device data processing.

    Main Methods:

    • Investigated new variants of supervised and adversarial learning.
    • Developed end-to-end methods for optimizing privacy-preserving feature mappings and predictive models.
    • Focused on minimizing computational burden for on-device desensitization.

    Main Results:

    • Experimental results on mobile sensing and face datasets validate the proposed methods.
    • The models successfully maintain the utility of predictive models.
    • Sensitive information predictions were significantly degraded, ensuring privacy.

    Conclusions:

    • The developed techniques effectively remove sensitive information from data prior to application use.
    • On-device data desensitization is achievable with low computational overhead.
    • The approach offers a practical solution for balancing data utility and privacy in Big Data applications.