Abstract Various sensors in Internet of things capture many images, and there is growing concern about their secure storing and sharing. Compressed sensing (CS) is a promising solution for this problem. However, traditional CS-based security frameworks only provide computational secrecy with high reconstruction complexities. In this paper, we propose a low-complexity privacy preserving scheme based on CS and non-negative matrix factorization (NMF) to protect image privacy while maintaining the utility of data. Specifically, CS is used to compress and encrypt the data, and then noise is added to improve security. At the same time, the basis matrix generated by NMF is used to construct a decoding matrix and a decryption matrix. For legitimate users with the decoding matrix, the low-dimensional data of the original signal can be obtained through simple matrix multiplication without complex reconstruction, which can be used for subsequent mining processing. For legitimate users with the decryption matrix, the approximation of the original signal can be obtained by only one-time matrix multiplication. Compared with other traditional schemes, the proposed one avoids the complex reconstruction and ensures the utility of data without revealing the privacy. Experiments and analyses verify the merits of our design in both privacy protection and computational complexity.

中文翻译：

---

基于压缩感知和非负矩阵分解的图像数据低复杂度隐私保护方案

摘要 物联网中的各种传感器捕获了大量图像，其安全存储和共享越来越受到关注。压缩感知 (CS) 是解决此问题的有前途的解决方案。然而，传统的基于 CS 的安全框架仅提供具有高重建复杂性的计算保密性。在本文中，我们提出了一种基于 CS 和非负矩阵分解 (NMF) 的低复杂度隐私保护方案，以在保持数据效用的同时保护图像隐私。具体来说，就是使用CS对数据进行压缩和加密，然后加入噪声来提高安全性。同时利用NMF生成的基矩阵构造解码矩阵和解密矩阵。对于具有解码矩阵的合法用户，通过简单的矩阵乘法可以得到原始信号的低维数据，无需复杂的重构，可用于后续的挖掘处理。对于具有解密矩阵的合法用户，只需进行一次矩阵乘法就可以得到原始信号的近似值。与其他传统方案相比，所提出的方案避免了复杂的重建，在不泄露隐私的情况下保证了数据的实用性。实验和分析验证了我们设计在隐私保护和计算复杂性方面的优点。与其他传统方案相比，所提出的方案避免了复杂的重建，在不泄露隐私的情况下保证了数据的实用性。实验和分析验证了我们设计在隐私保护和计算复杂性方面的优点。与其他传统方案相比，所提出的方案避免了复杂的重建，在不泄露隐私的情况下保证了数据的实用性。实验和分析验证了我们设计在隐私保护和计算复杂性方面的优点。