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Protecting Machine Learning Insights: Ensuring Data Privacy with Advanced Privacy-Preserving Techniques

EasyChair Preprint no. 12438

8 pagesDate: March 10, 2024


With the increasing reliance on machine learning (ML) for extracting valuable insights from data, the need to safeguard sensitive information has become paramount. This paper explores advanced privacy-preserving techniques aimed at securing ML insights and ensuring data privacy. We delve into cryptographic methods, federated learning, and differential privacy, offering a comprehensive overview of their applications in the ML landscape. Cryptographic techniques such as homomorphic encryption enable computations on encrypted data, ensuring that sensitive information remains confidential throughout the ML process. Federated learning facilitates model training across decentralized devices without centralizing raw data, preserving user privacy. Differential privacy introduces noise to individual data points, striking a balance between accurate model training and safeguarding individual contributions.

Keyphrases: cryptographic methods, data privacy, differential privacy, Federated Learning, machine learning, Privacy-preserving techniques

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Basit Abbas},
  title = {Protecting Machine Learning Insights: Ensuring Data Privacy with Advanced Privacy-Preserving Techniques},
  howpublished = {EasyChair Preprint no. 12438},

  year = {EasyChair, 2024}}
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