Survey of artificial intelligence data security and privacy protection

Artificial intelligence and deep learning algorithms are developing rapidly. These emerging techniques have been widely used in audio and video recognition, natural language processing and other fields. However, in recent years, researchers have found that there are many security risks in the current mainstream artificial intelligence model, and these problems will limit the development of AI. Therefore, the data security and privacy protection was studied in AI. For data and privacy leakage, the model output based and model update based problem of data leakage were studied. In the model output based problem of data leakage, the principles and research status of model extraction attack, model inversion attack and membership inference attack were discussed. In the model update based problem of data leakage, how attackers steal private data in the process of distributed training was discussed. For data and privacy protection, three kinds of defense methods, namely model structure defense, information confusion defense and query control defense were studied. In summarize, the theoretical foundations, classic algorithms of data inference attack techniques were introduced. A few research efforts on the defense techniques were described in order to provoke further research efforts in this critical area.