高效隐私保障下的反学习
Efficient Unlearning with Privacy Guarantees
AI总结：
本文提出EUPG框架，通过预训练保护数据的模型实现高效反学习，提供隐私保障，降低计算和存储成本，与精确反学习方法相比效果相当。
摘要：
隐私保护法规如GDPR赋予个人要求删除其个人数据的权利，不仅限于数据库，还包括训练其的机器学习（ML）模型。机器反学习已成为实现模型遗忘的数据实例的实用手段。尽管一些现有的机器反学习方法保证了精确遗忘，但通常在计算上是昂贵的。另一方面，更经济的方法不提供遗忘保证，并且只能应用于特定的ML模型。在本文中，我们提出了高效反学习与隐私保障（EUPG），一种新颖的机器反学习框架，为被反学习的数据的个人提供正式的隐私保障。EUPG涉及在使用隐私模型保护的数据上预训练ML模型，并使其能够通过所用隐私模型提供的隐私保障实现高效反学习。通过在四个异构数据集上进行实证评估，这些数据集使用k-匿名性和ε-差分隐私作为隐私模型，我们的方法展示了与精确反学习方法相当的效用和遗忘效果，同时显著降低了计算和存储成本。我们的代码可在https://github.com/najeebjebreel/EUPG上获得。
英文摘要：
Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML) models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data instances seen during training. Although some existing machine unlearning methods guarantee exact forgetting, they are typically costly in computational terms. On the other hand, more affordable methods do not offer forgetting guarantees and are applicable only to specific ML models. In this paper, we present \emph{efficient unlearning with privacy guarantees} (EUPG), a novel machine unlearning framework that offers formal privacy guarantees to individuals whose data are being unlearned. EUPG involves pre-training ML models on data protected using privacy models, and it enables {\em efficient unlearning with the privacy guarantees offered by the privacy models in use}. Through empirical evaluation on four heterogeneous data sets protected with $k$-anonymity and $ε$-differential privacy as privacy models, our approach demonstrates utility and forgetting effectiveness comparable to those of exact unlearning methods, while significantly reducing computational and storage costs. Our code is available at https://github.com/najeebjebreel/EUPG.
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。
