Abstract
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%.
Highlights
With the rapid development of information technology and multimedia technology, it is easier to obtain and share face digital images
Gray histogram reflects the relationship between the frequency of gray level pixels and gray level in an image, the gray histogram cannot reflect the specific distribution of image pixels but reflect the impact of Laplace noise on the image as a statistical result
To solve the privacy protection problem of face image publishing, this paper proposes to use the Laplace mechanism of differential privacy to add noise in the image, so that this should protect the sensitive information in the face image
Summary
With the rapid development of information technology and multimedia technology, it is easier to obtain and share face digital images. To reduce the impact of noise on the original image and improve the usability of published images, this paper proposes a differential privacy protection method for image publishing It is inspired by the noncorrelation of the values in the image matrix; this paper tries to use image segmentation technology to transform the image gray matrix into 1D ordered data stream and use the sliding window model to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated which is used to solve the problem of image privacy protection This method satisfies the differential privacy and has high usability of the published image
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