De-identifying transmission system using wireless channel as differential privacy noise and deep neural networks

Abstract

In order to deal with the unprecedented problem of data privacy in deep-learning based computer vision technologies, we propose a de-identifying transmission system that utilizes a wireless channel as differential privacy (DP) noise and is constructed using deep neural networks. By following the Gaussian mechanism of DP, we present that the signal received at a receiver can be considered as a Gaussian mechanism. Then, we discuss the relationship between the signal-to-noise ratio of a received signal and the privacy budget of DP, and introduce how to control transmit power to achieve a specific privacy budget. The proposed system can be divided into three parts, named transmitter, wireless channel, and receiver. The transmitter and receiver are constructed by using deep-learning networks. The transmitter consists of an image encoder network based on a neural network and a power control module that controls the transmit power for adjusting the level of de-identification. The wireless channel acts as differential privacy noise, and anonymizes the transmitted image feature vector extracted from the transmitter's image encoder network. The receiver includes a post-processing network and an image decoder network, which are also implemented by using deep neural networks. The post-processing network is proposed for high-quality decoded face images at the receiver, which maps received feature vectors perturbed by a wireless channel back into the latent space of deep-learning based image encoder and decoder networks. Finally, extensive qualitative and quantitative evaluations confirm that the proposed system can well de-identify transmitted face images only by controlling the transmit power while maintaining the usefulness of decoded face images. The proposed system shows that the Recall@1 is smaller than 5.2 and the face detection probability is larger than 90 % at SNR=2 dB. Since the de-identification process is performed by wireless channel noise, the proposed system does not require de-identification processing at a transmitter, and thus there is no burden on the transmitter to anonymize face images. Moreover, the additional advantage of this proposed system is that the level of de-identification can be controlled only by changing the transmit power. This proposed de-identification system can be utilized in a wireless image acquisition scenario where images are captured from a wireless edge, such as a CCTV camera, and then sent to a server while protecting people's privacy.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).