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Privacy and Security for Large Language Models
As the deployment of AI technologies surges, the need to safeguard privacy and security in the use of large language models (LLMs) is more crucial than ever. Professionals face the challenge of leveraging the immense power of LLMs for personalized applications while ensuring stringent data privacy and security. The stakes are high, as privacy breaches and data leaks can lead to significant reputational and financial repercussions. This book serves as a much-needed guide to addressing these pressing concerns. Dr. Baihan Lin offers a comprehensive exploration of privacy-preserving and security techniques like differential privacy, federated learning, and homomorphic encryption, applied specifi...
Sims Index to Land Grants in West Virginia
"The land grants listed herein were made by Lord Fairfax prior to the creation of the Virginia Land Office; by the Commonwealth of Virginia, of lands now embracing the State of West Virginia; and, by the State of West Virginia, under its first Constitution."--Page [1].
Hands-On Differential Privacy
Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and unders...
Congressional Record
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