Survey on Privacy Protection Solutions for Recommended Applications

In the context of the era of big data,various industries want to train recommendation models based on user behavior data to provide users with accurate recommendations.The common characteristics of the used data are huge amount,carrying sensitive information,and easy to obtain.The recommendation system is sharing users' private data in real time while bringing accurate recommendation and market profit.Differential privacy,as a privacy protection technology,can cleverly solve the problem of privacy leakage in recommendation applications.No matter the attacker has any relevant background knowledge,differential privacy strictly defines privacy protection,and provides quantitative evaluation methods to ensure that the level of privacy protection provided by the data set is comparable.First,the concept of differential privacy and the research on mainstream recommendation algorithms is briefly described.Second,the combined application of differential privacy and recommendation algorithms is analyzed,such as matrix factorization,deep learning recommendation,and collaborative filtering.A large number of comparative experiments have been conducted on recommendation algorithms based on differential privacy technology.Then the application scenarios of the combination of differential privacy and each recommendation algorithm and the remaining problems are discussed.Finally,effective suggestions are put forward for the future development direction of the recommendation algorithm based on differential privacy.