As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one-dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort-SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the ε -differential privacy. Therefore, this paper proposes a sorting method-SAS method, which satisfies the ε -differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort-SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1-sroce is improved by 55.62% and 16.55%.

The privacy protection for face images aims to prevent attackers from accurately identifying target persons through face recognition. Inspired by goal-driven reasoning (reverse reasoning), this paper designs a goal-driven algorithm of local privacy protection for sensitive areas in multiface images (face areas) under the interactive framework of face recognition algorithm, regional growth, and differential privacy. The designed algorithm, named privacy protection for sensitive areas (PPSA), is realized in the following manner: Firstly, the multitask cascaded convolutional network (MTCNN) was adopted to recognize the region and landmark of each face. If the landmark overlaps a subgraph divided from the original image, the subgraph will be taken as the seed for regional growth in the face area, following the growth criterion of the fusion similarity measurement mechanism (FSMM). Different from single-face privacy protection, multiface privacy protection needs to deal with an unknown number of faces. Thus, the allocation of the privacy budget ε directly affects the operation effect of the PPSA algorithm. In our scheme, the total privacy budget ε is divided into two parts: ε_1 and ε_2. The former is evenly allocated to each seed, according to the estimated number of faces ρ contained in the image, while the latter is allocated to the other areas that may consume the privacy budget through dichotomization. Unlike the Laplacian (LAP) algorithm, the noise error of the PPSA algorithm will not change with the image size, for the privacy protection is limited to the face area. The results show that the PPSA algorithm meets the requirements ε-Differential privacy, and image classification is realized by using different image privacy protection algorithms in different human face databases. The verification results show that the accuracy of the PPSA algorithm is improved by at least 16.1%, the recall rate is improved by at least 2.3%, and F1-score is improved by at least 15.2%.