To determine whether images on the crowdsourcing server meet the mobile user’s requirement, an auditing protocol is desired to check these images. However, before paying for images, the mobile user typically cannot download them for checking. Moreover, since mobiles are usually low-power devices and the crowdsourcing server has to handle a large number of mobile users, the auditing protocol should be lightweight. To address the above security and efficiency issues, we propose a novel noninteractive lightweight privacy-preserving auditing protocol on images in mobile crowdsourcing networks, called NLPAS. Since NLPAS allows the mobile user to check images on the crowdsourcing server without downloading them, the newly designed protocol can provide privacy protection for these images. At the same time, NLPAS uses the binary convolutional neural network for extracting features from images and designs a novel privacy-preserving Hamming distance computation algorithm for determining whether these images on the crowdsourcing server meet the mobile user’s requirement. Since these two techniques are both lightweight, NLPAS can audit images on the crowdsourcing server in a privacy-preserving manner while still enjoying high efficiency. Experimental results show that NLPAS is feasible for real-world applications.

中文翻译：

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移动众包网络中图像的非交互式轻量级隐私保护审计

为了确定众包服务器上的图像是否满足移动用户的需求，需要审核协议来检查这些图像。但是，在为图像付费之前，移动用户通常无法下载图像进行检查。此外，由于移动设备通常是低功耗设备，并且众包服务器必须处理大量移动用户，因此审核协议应轻巧。为了解决上述安全性和效率问题，我们提出了一种新颖的非交互式轻量级隐私保护隐私协议审计协议，称为NLPAS。由于NLPAS允许移动用户无需下载即可检查众包服务器上的图像，因此新设计的协议可以为这些图像提供隐私保护。同时，NLPAS使用二进制卷积神经网络从图像中提取特征，并设计了一种新颖的隐私保护汉明距离计算算法，用于确定众包服务器上的这些图像是否满足移动用户的需求。由于这两种技术都是轻量级的，因此NLPAS可以以隐私保护的方式审核众包服务器上的图像，同时仍能保持较高的效率。实验结果表明，NLPAS对于实际应用是可行的。NLPAS可以在保护隐私的同时，在众包服务器上审计图像，同时仍然保持高效率。实验结果表明，NLPAS对于实际应用是可行的。NLPAS可以在保护隐私的同时，在众包服务器上审核图像，同时仍然保持较高的效率。实验结果表明，NLPAS对于实际应用是可行的。