Unlocking Product Innovation with Differential Privacy in LLMs
Tejas Cyber Network brings to you a thought-provoking technical session on "Unlocking Product Innovation with Differential Privacy in LLMs" by Akhilesh Srivastava who is the Product Technical PM leader.
Session Brief - As the integration of Large Language Models (LLMs) into products becomes widespread, ensuring user privacy remains a critical challenge. This session will explore how Differential Privacy (DP) can be leveraged to protect sensitive information while still generating high-quality outputs. We'll dive into real-world use cases and innovative approaches where differential privacy plays a vital role in balancing data utility and privacy in LLM applications.
(Disclaimer: All views are his own and do not reflect the stand of his current or past employers.)
Agenda -
1) Introduction to Differential Privacy - Interactive way of learning it together 2) Use Cases of Differential Privacy in LLMs
More to add.
Speaker Bio - Akhilesh is a strategic privacy leader with a wealth of experience as a Product Technical PM in tech companies like Meta and Amazon. Over his 19-year journey, he has been at the forefront of innovation across diverse domains, including Privacy, Fin-Tech (Payments, Regulation), Ads, Insurance, and e-commerce. Passionate about AI governance, Privacy, AI Ethics, GenAI/LLM, AWS, and Big Data, Akhilesh has driven multi-million-dollar impacts across various product suites, serving billions of customers globally on a large scale.
He is an executive member of Tejas Cyber Global Professionals Network. Additionally, he actively volunteers in various nonprofit community initiatives, providing mentorship.
He is certified in MIT Sloan's AI-Implications for Business Strategy.
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