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面向人脸图像发布的差分隐私保护方法

张啸剑,付聪聪,孟小峰(河南财经政法大学;中国人民大学)

摘 要
目的:由于人脸图像蕴含着丰富的个人敏感信息,直接发布出来可能会造成个人的隐私泄露。为了保护人脸图像中的隐私信息,本文提出了一种基于傅里叶变换与差分隐私技术相结合的人脸图像发布算法。方法:该算法将人脸图像作为实数域二维矩阵,充分利用离散傅里叶变换技术压缩图像。为了有效均衡由拉普拉斯机制引起的噪音误差与由傅里叶变换导致的重构误差,引入一种基于指数机制的傅里叶系数选择方法,它能够在不同的系数空间中挑选出合理的傅里叶系数来压缩人脸图像,然后利用拉普拉斯机制对所挑选出的系数添加噪音,进而使整个处理过程满足ε-差分隐私。结果:基于四种真实人脸图像数据集采用SVM分类技术验证算法的正确性。从算法的准确率、召回率,以及F1-Score度量结果显示,本文所提出的人脸图像发布算法均具有较高的正确性。结论:实验结果表明,本文算法能够实现满足ε-差分隐私的敏感人脸图像发布,图像分类验证其具有较高的可用性。该算法鲁棒性好,是一种有效的隐私人脸图像发布方法。
关键词
Facial Image Publication with Differential Privacy

Zhang Xiaojian,Fu Congcong,Meng Xiaofeng(School of Computer Information Engineering,Henan University of Economics and Law;School of Informatica,Renmin University of China)

Abstract
Objective Facial image publication in a direct way may lead to privacy leakage, because facial images are inherently sensitive. To protect the private information in facial image, this paper proposes an efficient algorithm based on Fourier transform combined with differential privacy. Method First, this algorithm employs the real-valued matrix to model facial image, in which each cell corresponds to each pixel point of image. After that, this algorithm relies on Fourier transform based on the matrix to extract the Fourier coefficients, and then uses the Laplace mechanism to inject noise into each coefficient to ensure differential privacy. Finally, this algorithm uses Fourier inverse transform to reconstruct the noisy facial image. However, in this process, we encounter two sources of errors: 1) the Laplace error (LE) due to Laplace noise injected, and 2) the reconstruction error (RE) caused by lossy compression. The trade-off between the LE and the AE is vital to the final accuracy of a sanitized facial image. To address this problem, this algorithm samples k elements in different candidate coefficient set via Exponential mechanism. For the samples, we add the Laplace noise to meet differential privacy. Result SVM classification on four real facial image datasets show that our proposed algorithm significantly outperforms existing solutions in terms of precision, recall, and F1-score. Conclusion Experimental results show that the proposed algorithm can effectively overcome the privacy leakage of facial image due to publication, the released facial images are accurate, and satisfy ε–differential privacy. The algorithm has good robustness, and it is an effective private facial image releasing method.
Keywords
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