With the widespread application of facial image recognition in fields such as finance, social
media, and transportation, the privacy data of facial images is also frequently leaked. In …

The training of state-of-the-art deep learning models generally requires significant high-
quality data, including personal and sensitive data. To ensure privacy of the sensitive data …

Although the current face de-recognition algorithm can effectively protect the user's identity,
it will change the original attributes of the image, destroy the visual effect, or change the …

To solve the problem that privacy data are easy to leak in the application of face recognition
technology in apps, a method which is based on differential privacy for privacy security …

The widespread use of face recognition systems has raised concerns about privacy and
security. In this paper, we proposed multi-attribute privacy-preserving generative ad …

In big-data era, large amount of facial images could be used to breach the face identification
system, which demands effective Face IDentification Differential Privacy (FaceIDP) of the …

Over the recent years, a massive number of face images are widely used in each field of
Internet, which makes the privacy leakage problem due to the abuse of facial images …

Recent research has established the possibility of deducing soft-biometric attributes such as
age, gender, and race from an individual's face image with high accuracy. However, this …

Face image features represent significant user privacy concerns. Face images cannot be
privately transferred under existing privacy protection methods, and data across various …

There are many privacy protection methods in the field of artificial intelligence. Firstly, this
paper summarizes the related secure multi-party privacy computing methods, image …