被遗忘设计项目技术报告：针对机器学习的定向混淆
Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning
AI总结：
本文提出“被遗忘设计”方法，在训练中通过定向混淆保护隐私，相比机器遗忘更主动，实验表明可大幅降低隐私风险并保持模型性能。
摘要：
隐私权在各项人权宣言中均有体现，但在人工智能（AI）时代面临着新的挑战。本文探讨了AI系统中被遗忘权（RTBF）的概念，并将其与传统的删除数据方法进行对比。我们提出了“被遗忘设计”（Forgotten by Design），这是一种在AI模型训练过程中整合实例特定混淆技术的主动隐私保护方法。与在训练后修改模型的机器遗忘不同，我们的方法从源头上防止敏感数据被嵌入模型。利用LIRA成员推理攻击，我们识别出易受攻击的数据点，并提出结合加性梯度噪声和加权方案的防御措施。我们在CIFAR-10数据集上的实验表明，我们的技术将隐私风险降低了至少一个数量级，同时保持了模型准确性（在95%的显著性水平下）。此外，我们提出了隐私-效用权衡的可视化方法，为平衡隐私风险和模型准确性提供了一个清晰的框架。这项工作有助于开发与人类主动遗忘的认知过程相一致的隐私保护AI系统，为保护敏感信息和确保遵守隐私法规提供了稳健的框架。
英文摘要：
The right to privacy, enshrined in various human rights declarations, faces new challenges in the age of artificial intelligence (AI). This paper explores the concept of the Right to be Forgotten (RTBF) within AI systems, contrasting it with traditional data erasure methods. We introduce Forgotten by Design, a proactive approach to privacy preservation that integrates instance-specific obfuscation techniques during the AI model training process. Unlike machine unlearning, which modifies models post-training, our method prevents sensitive data from being embedded in the first place. Using the LIRA membership inference attack, we identify vulnerable data points and propose defenses that combine additive gradient noise and weighting schemes. Our experiments on the CIFAR-10 dataset demonstrate that our techniques reduce privacy risks by at least an order of magnitude while maintaining model accuracy (at 95% significance). Additionally, we present visualization methods for the privacy-utility trade-off, providing a clear framework for balancing privacy risk and model accuracy. This work contributes to the development of privacy-preserving AI systems that align with human cognitive processes of motivated forgetting, offering a robust framework for safeguarding sensitive information and ensuring compliance with privacy regulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
