This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.

本研究针对机器遗忘在金融和电子商务应用中的安全及数据攻击进行调查，分析了成员推断攻击和数据重建攻击等隐私威胁。提出了包括差分隐私和零知识证明等一系列防御策略，以增强机器遗忘机制的安全性，保障数据完整性和隐私，并强调了针对不断演变的攻击向量的持续研究和创新的重要性。

金融和电子商务领域机器遗忘的安全与数据攻击调查