Cloud-based expert systems are highly emerging nowadays. However, the data owners and cloud service providers are not in the same trusted domain in practice. For the sake of data privacy, sensitive data usually has to be encrypted before outsourcing which makes effective cloud utilization a challenging task. Taking this concern into account, we propose a novel cloud-based approach to securely recognize human activities. A few schemes exist in the literature for secure recognition. However, they suffer from the problem of constrained data and are vulnerable to re-identification attack, where advanced deep learning models are used to predict an object’s identity. We address these problems by considering color and depth data, and securing them using position based superpixel transformation. The proposed transformation is designed by actively involving additional noise while resizing the underlying image. Due to this, a higher degree of obfuscation is achieved. Further, in spite of securing the complete video, we secure only four images, that is, one motion history image and three depth motion maps which are highly saving the data overhead. The recognition is performed using a four stream deep Convolutional Neural Network (CNN), where each stream is based on pre-trained MobileNet architecture. Experimental results show that the proposed approach is the best suitable candidate in “security-recognition accuracy (%)” trade-off relation among other image obfuscation as well as state-of-the-art schemes. Moreover, a number of security tests and analyses demonstrate robustness of the proposed approach.

如今，基于云的专家系统正在迅速兴起。但是，实际上，数据所有者和云服务提供商不在同一个受信任域中。为了数据隐私，通常必须在外包之前对敏感数据进行加密，这使得有效利用云成为一项艰巨的任务。考虑到此问题，我们提出了一种新颖的基于云的方法来安全地识别人类活动。文献中存在一些用于安全识别的方案。但是，它们遭受数据约束的问题，并且容易受到重新识别攻击的攻击，在该攻击中，高级深度学习模型用于预测对象的身份。我们通过考虑颜色和深度数据，并使用基于位置的超像素变换来保护它们，从而解决了这些问题。通过在调整基础图像大小的同时积极涉及其他噪声来设计提出的转换。因此，实现了更高程度的混淆。此外，尽管保护了完整的视频，我们也仅保护了四个图像，即一个运动历史图像和三个深度运动图，它们极大地节省了数据开销。使用四流深度卷积神经网络（CNN）进行识别，其中每个流均基于预训练的MobileNet架构。实验结果表明，该方法在其他图像模糊处理和最新方案之间的“安全-识别精度（％）”折衷关系中是最合适的选择。此外，许多安全测试和分析证明了所提出方法的鲁棒性。