@Article{jihpp.2022.039284,
AUTHOR = {Ling Zhang, Lina Nie, Leyan Yu},
TITLE = {A Survey of Privacy Preservation for Deep Learning Applications},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {4},
YEAR = {2022},
NUMBER = {2},
PAGES = {69--78},
URL = {http://www.techscience.com/jihpp/v4n2/52314},
ISSN = {2637-4226},
ABSTRACT = {Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly reviews the privacy leakage problems that exist when using deep learning, then introduces deep learning algorithms that support privacy protection, compares and looks forward to these algorithms, and summarizes this aspect’s development.},
DOI = {10.32604/jihpp.2022.039284}
}