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The MLSecOps Podcast
Privacy Engineering: Safeguarding AI & ML Systems in a Data-Driven Era; With Guest Katharine Jarmul
Jul 12, 2023
In this episode, renowned data scientist Katharine Jarmul discusses the risks of data privacy and security in ML models. They touch on topics such as OpenAI's ChatGPT, GDPR, challenges faced by organizations, privacy by design, and reputational risk. They emphasize the need for auditability, consent questions, and population selection, as well as promoting a culture of privacy champions. Building models in a secure and private way is crucial, and listeners have a chance to win Katharine's book on practical data privacy.
46:44
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Quick takeaways
- Privacy plays a crucial role in protecting machine learning models from privacy breaches by implementing data minimization and privacy-enhancing techniques during training.
- Data breaches and privacy violations can lead to severe reputational damage, emphasizing the need to prioritize privacy with robust measures and establish a culture of privacy champions.
Deep dives
The Importance of Privacy in Machine Learning Models
Privacy plays a vital role in ensuring the security of machine learning models. With the increasing use of personal data in natural language processing and other ML applications, there is a risk of memorization or overfitting of private information, which can lead to privacy breaches. It is crucial to consider data minimization, tokenization, and other privacy-enhancing techniques during the model training and feature engineering stages. Additionally, organizations should focus on auditable and automated processes to ensure compliance with privacy regulations. Federated learning and encrypted learning are advanced methods that can help protect privacy during training. Privacy engineering teams and privacy champions within organizations can facilitate the integration of privacy by design principles throughout the ML lifecycle.
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Discover
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