为儿童AI应用设计隐私保护框架，覆盖数据全生命周期
A Privacy by Design Framework for Large Language Model-Based Applications for Children
- 基于GDPR、COPPA等法规构建全流程隐私设计框架
- 提出数据收集到运营监控各阶段的具体防护策略
- 适合教育类AI开发者与儿童数字产品设计者参考
儿童正越来越多地使用基于人工智能的技术，但隐私风险日益突出。尽管现有法规要求企业实施保护措施，实际操作仍具挑战。本文提出一种基于隐私设计（Privacy-by-Design）的框架，指导开发者采取前瞻性、风险规避的设计方法。框架整合欧盟GDPR、加拿大PIPEDA、美国COPPA等法规原则，并将其映射至大语言模型（LLM）应用的全生命周期：数据采集、模型训练、运行监控与持续验证。针对每个阶段，梳理近期学术文献中的技术与组织控制措施，帮助AI服务提供者降低隐私风险并符合法律要求。同时结合联合国《儿童权利公约》（UNCRC）、英国年龄适宜设计代码（AADC）及最新研究，制定面向儿童的设计准则。通过一个面向13岁以下儿童的LLM教育辅导系统案例研究，验证框架可行性。结果表明，通过采用技术控制与适龄设计决策贯穿模型全生命周期，可有效实现隐私保护与合规。
原文摘要 · Abstract (English)
Children are increasingly using technologies powered by Artificial Intelligence (AI). However, there are growing concerns about privacy risks, particularly for children. Although existing privacy regulations require companies and organizations to implement protections, doing so can be challenging in practice. To address this challenge, this article proposes a framework based on Privacy-by-Design (PbD), which guides designers and developers to take on a proactive and risk-averse approach to technology design. Our framework includes principles from several privacy regulations, such as the General Data Protection Regulation (GDPR) from the European Union, the Personal Information Protection and Electronic Documents Act (PIPEDA) from Canada, and the Children's Online Privacy Protection Act (COPPA) from the United States. We map these principles to various stages of applications that use Large Language Models (LLMs), including data collection, model training, operational monitoring, and ongoing validation. For each stage, we discuss the operational controls found in the recent academic literature to help AI service providers and developers reduce privacy risks while meeting legal standards. In addition, the framework includes design guidelines for children, drawing from the United Nations Convention on the Rights of the Child (UNCRC), the UK's Age-Appropriate Design Code (AADC), and recent academic research. To demonstrate how this framework can be applied in practice, we present a case study of an LLM-based educational tutor for children under 13. Through our analysis and the case study, we show that by using data protection strategies such as technical and organizational controls and making age-appropriate design decisions throughout the LLM life cycle, we can support the development of AI applications for children that provide privacy protections and comply with legal requirements.
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