保护隐私的部分图像共享

Jianping He, Bin Liu, Xuan Bao, Hongxia Jin, G. Kesidis
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引用次数: 1

摘要

通过在线社交网络分享照片已成为一种日益流行的时尚。然而,当敏感照片被不当分享时,用户的隐私可能会受到威胁。本文提出了一种图像数据的动态隐私保护技术(命名为puppy),数据所有者为照片/图像的敏感对象(人脸、社会保险号等)规定小的私有区域,并针对不同的个体为这些局部区域设置不同的共享策略。小狗是基于压缩图像数据的优化可逆矩阵摄动。因此,它可以自然地支持经常使用的图像转换。我们的实验表明,我们的解决方案对隐私保护是有效的,并且对部分图像共享只产生很小的开销。
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On Privacy Preserving Partial Image Sharing
Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.
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