The invention discloses a multi-level weighted face image
differential privacy protection method. The method comprises the following steps: firstly, acquiring a
wavelet coefficient of a face image by applying multistage
wavelet transform; secondly, calculating the influence of the
noise of the
wavelet coefficient of each sub-band of each level on the face features by using partial derivative; then, respectively quantizing privacy budget contributed by
noise disturbing each level of weighted wavelet coefficient to face features and a local data utility
loss function, decomposing a constraint
optimization problem into a plurality of sub-problems level by level, and solving a preliminarily optimized
noise scale parameter; then, according to the preliminarily optimized scale parameters, reconstructing a global constraint
optimization problem, and further solving to obtain optimized scale parameters; and finally, obtaining a disturbed face image and a face
feature vector according to a multi-level weighted
differential privacy mechanism, hierarchical sampling of noise, disturbance of wavelet coefficients and inverse wavelet transformation. According to the method, sensitive face features in the public face image can be effectively protected, and meanwhile, the visual effect of the image is reserved.