Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce

Machine learning in financial and e-commerce sector employs vast amounts of data are used to predict trends, detect fraud, and optimize decision-making processes. However, as these models become more widespread, concerns over security and privacy have also increased. In response to such challenges, machine unlearning has been introduced as a solution to enable models to forget specific data points when necessary, particularly for compliance with data regulations like the General Data Protection Regulation (GDPR). While machine unlearning provides an avenue for users to request the deletion of data from ML models, it also introduces new vulnerabilities to both privacy and security. Privacy and security attacks on machine unlearning are growing areas of concern, especially in sensitive financial applications where personal data is paramount. Two main categories of attacks can exploit this process: privacy attacks and security attacks. Privacy attacks target the confidentiality of data by attempting to reveal sensitive information, whereas security attacks aim to compromise the integrity and functionality of the machine unlearning process. In this paper, we aim to survey the types of privacy and security data attacks specific to machine unlearning in financial applications.