Recent legislations, such as the European General Data Protection Regulation (GDPR), require user data holders to guarantee the individual’s right to be forgotten. This means that user data holders must completely delete user data upon request. However, in the field of machine learning, it is not possible to simply remove these data from the back-end database wherein the training dataset is stored, because the machine learning model still retains this data information. Retraining the model using a dataset with these data removed can overcome this problem; however, this can lead to expensive computational overheads. In order to remedy this shortcoming, we propose two effective methods to help model owners or data holders remove private data from a trained model. The first method uses an elastic weight consolidation (EWC) constraint term and a modified loss function to neutralize the data to be removed. The second method approximates the posterior distribution of the model as a Gaussian distribution, and the model after unlearning is computed by decreasingly matching the moment (DMM) of the posterior distribution of the neural network trained on all data and the data to be removed. Finally, we conducted experiments on three standard datasets using backdoor attacks as the evaluation metric. The results show that both methods are effective in removing backdoor triggers in deep learning models. Specifically, EWC can reduce the success rate of backdoor attacks to 0. IMM can ensure that the model prediction accuracy is higher than 80% and keep the success rate of backdoor attacks below 10%.

Physically distributed voters can vote online through an electronic voting (e-voting) system. It can outsource the counting work to the cloud when the system is overloaded. However, this kind of outsourcing may lead to some security problems such as anonymity, privacy and fairness, etc. Suppose servers in the cloud have no incentives to deviate from the e-voting system, these security problems can be effectively solved. In this paper, we assume that servers in cloud are rational, and try to maximise their utilities. We look for incentives for rational servers not to deviate from the e-voting system. Here no deviation means rational servers prefer to cooperate in the e-voting system. Simulation results of our evolution model show that the cooperation level is high after certain rounds. Finally, we put forward a rational e-voting protocol based on the above results and prove that the system is secure under proper assumptions.