Face Image Publication Based on Differential Privacy

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%.

Differential Privacy Protection of Face Images Based on Region Growing

“…The sub-image size affects the division results, which in turn influence the total noise generated by the algorithm. In addition to LAP, RGP, and ARGP, XXX's sliding window publication (SWP) algorithm [1] Experimental results show that the total noise generated by an algorithm change with the sub-image size. When the subimages are too large or too small, the images after privacy protection will have a low availability.…”

“…None of these methods can avoid the reconstruction error RE(X m×n ′ ). Therefore, this paper inherits the approach of Liu et al [1]: rather than change the data in the 2D matrix, implement the reconstruction from the perspective of structure. Without changing the original data, this approach effectively avoids RE(X m×n ′ ).…”

“…‖M(X) − M(X′)‖ p be the value of ΔQ GS under norm p, with 1 ≤ p ≤ +∞. Suppose ΔQ GS (p) is a decreasing function, i.e., ΔQ GS (p) ≤ ΔQ GS (1) . Then, when the norm is p,…”

“…In this paper, the values are extracted from each grayscale matrix by formula (1). Thus, the values in the grayscale matrices are approximate.…”

“…These digital images contain lots of sensitive personal information. If they are collected and analyzed by malicious third-parties, privacy leak and other losses will be beyond measure [1].…”