Privacy protection technology in e-commerce based on dilated convolution network model

Abstract

This paper aims to investigate and advance the privacy protection techniques within the e-commerce domain by leveraging the dilated convolutional network (DCN) model. It endeavors to elevate the safeguarding of user data privacy to a higher level of security. The study introduces a privacy protection methodology grounded on the DCN model. Firstly, it delves into an analysis of the threats and attack vectors concerning privacy security in e-commerce settings. Subsequently, it devises a network model integrating DCN, attention mechanism, and gating mechanism, tailored to fortify the privacy of user data. Additionally, the paper incorporates advanced technologies such as differential privacy and encryption algorithms to augment the data’s privacy protection capabilities. Finally, this paper uses the actual e-commerce dataset for experimental evaluation and compares the method proposed in this paper with other common privacy protection methods. The experimental results show that (1) the privacy protection technology based on the DCN model proposed in this paper has obvious advantages in reconstruction error, information utilization efficiency, and model efficiency. (2) The overall performance (accuracy, recall, and F1 value) of the privacy protection technology based on the DCN model proposed in this paper is better than that of the comparison method. The experimental findings outlined above underscore the promising potential and advantages of the DCN model in enhancing privacy protection within e-commerce platforms. This paper presents a robust privacy protection technology tailored for e-commerce platforms, which serves to bolster user trust and elevate the overall level of data privacy protection.