RUE: Realising Unlearning from the Perspective of Economics
MingJian Tang, Weiqi Wang, Chenhan Zhang, Shui Yu · 2023
Machine unlearning has quickly emerged as a technique to withdraw users’ data from the trained model to protect their privacy. Yet the cost of completely unlearning by retraining is high and the unlearning service is thus hard to proceed in the market. We work on the SISA method that greatly lowers the cost of unlearning as an example in this manuscript. We model the problem with a game theory model that balances customers’ benefit and the service provider’s profit, and the optimal price is acquired that both parties could accept. More specifically, in the game model, we calculate customers’ average waiting time with bulk service queueing model, and linearly estimate the customers’ benefit in terms of privacy from withdrawing their data. Our method RUE shows that both parties get more benefit or profit than others, which could make the unlearning service run smoothly when the concerns about the high price are removed. We also analyse the influence of the waiting time on the number of unlearning requests and on the price.