Privacy and security for large language models : hands-on privacy-preserving techniques for personalized AI / Baihan Lin

By:

Lin, Baihan

Material type:

TextPublication details: Santa Rosa : O'Reilly Media Inc., 2026Description: xx, 293 pISBN:

9781098160845

Subject(s):

DDC classification:

005.8 LIN-P

Summary: 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

Item type:

Books

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Current library	Call number	Status	Barcode
UMT Main Campus	005.8 LIN-P (Browse shelf(Opens below))	Available	153073

Browsing UMT Main Campus shelves Close shelf browser (Hides shelf browser)

Previous								Next
Previous	005.8 KOH-I Implementing cybersecurity :	005.8 LEL-C Cybersecurity tabletop exercises : from planning to execution /	005.8 LEV-C The computer virus handbook	005.8 LIN-P Privacy and security for large language models : hands-on privacy-preserving techniques for personalized AI /	005.8 LI--S Security intelligence :	005.8 MAI-N Network security	005.8 MAI-S Security planning & disaster recovery	Next

Index present

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

Eng

There are no comments on this title.

to post a comment.