January 2026
Beginner to intermediate
318 pages
3h 38m
Chinese
Overview
本作品已使用人工智能进行翻译。欢迎您提供反馈和意见：translation-feedback@oreilly.com
随着人工智能技术部署的激增，在使用大型语言模型（LLMs）时保护隐私和安全的需求比以往任何时候都更加重要。专业人员面临的挑战是，既要利用 LLMs 的巨大威力实现个性化应用，又要确保严格的数据隐私和安全。这其中的风险很大，因为隐私泄露和数据泄漏可能会导致重大的声誉和经济影响。
本书是解决这些紧迫问题的急需指南。Baihan Lin博士全面探讨了专门应用于LLMs的隐私保护和安全技术，如差分隐私、联合学习和同态加密。本书提供了实践代码示例、真实案例研究以及针对特定领域应用的强大微调方法，是在当今注重隐私的环境中开发安全、道德和个性化人工智能解决方案的重要资源。
通过阅读本书，您将
- 发现 LLMs 的隐私保护技术
- 学习个性化 LLM 的安全微调方法
- 了解安全部署策略和攻击防护
- 探索道德方面的考虑因素，如偏见和透明度
- 从医疗保健、金融等领域的实际案例研究中获得见解
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