An overview of machine unlearning - Chunxiao Li; Haipeng Jiang; Jiankang Chen; Yu Zhao; Shuxuan Fu; Fangming Jing; Yu Guo (High-Confidence Computing)
Nowadays, machine learning is widely used in various applications. Training a model requires huge amounts of data, but it can pose a threat to user privacy. With the growing concern for privacy, the “Right to be Forgotten” has been proposed, which means that users have the right to request that their personal information be removed from machine learning models. The emergence of machine unlearning is a response to this need. Implementing machine unlearning is not easy because simply deleting samples from a database does not allow the model to “forget” the data. Therefore, this paper summarises the definition of the machine unlearning formulation, process, deletion requests, design requirements and validation, algorithms, applications, and future perspectives, in the hope that it will help future researchers in machine unlearning.