LPDBN: A Privacy Preserving Scheme for Deep Belief Network
Yong Zeng, Tong Dong, Qingqi Pei, Jiale Liu, Jianfeng Ma · 2021
In recent years, the successful applications of deep learning technology bring serious privacy issues. Current countermeasures can achieve privacy protection by introducing differential privacy mechanisms to convolutional deep belief network, which will inevitably bring huge computational complexity in convolutional kernels. In this paper, we focus on designing a lightweight security privacy protection framework LPDBN, a novel Local differential Privacy binary pattern Deep Belief Network. We use the local binary pattern to extract texture information from the images instead of convolutional kernels, which greatly reduces the time complexity and data dimension. Meanwhile, the proposed framework can improve recognition performance under the same privacy protection intensity. The theorem analysis and experiments show the security and efficiency respectively.