With the popularity of personal devices, there are abundant valuable face image datasets in the industry, which provides opportunities for the development of visual models. However, privacy concerns related to identity sensitive information hinder face datasets sharing. Despite existing works dedicated to removing identity sensitive information from images, they either lack provable privacy guarantees or compromise crucial face dataset utilities, e.g., identity correlation and image naturalness. To overcome these weaknesses, we propose a novel face dataset publication scheme that protects face images by obfuscating face features. The obfuscated features still retain a certain level of correlation, allowing the protected dataset to be used for training. In the process of obfuscating the features, we design a novel metric differential privacy mechanism, which can enhance the correlation between features while ensuring privacy. Furthermore, we construct a latent diffusion model with identity and attribute as inputs to improve the naturalness of generated images. Extensive experimental results and theoretical analysis demonstrate our scheme significantly outperforms existing works in providing privacy protection while maintaining high dataset utility for downstream tasks.

In this paper, we propose an audio encryption scheme that supports differentiated decryption, called AES-AUDIO, in which an audio only needs to be encrypted once and can be decrypted into different resolutions as needed. First, we design four security levels, confidential, harsh, noisy, and clear, based on the audio resolution perceived by human auditory perception. Second, the audio data in decimal floating-point numbers (D-FPNs) are unfolded to 32 bits (B-FPNs). Third, we design a region of interest (RoI) encryption algorithm for the audio with the B-FPN format, where the result preserves some perceptual information as needed. Fourth, we construct the AES-AUDIO scheme based on the RoI encryption algorithm, which allows the audio to be encrypted once and then decrypted into different security levels. It supports changing parameters to alter the perception effect corresponding to the security level. Overall, it achieves a balance between the security and usability of the protected audio. User experiments verify that the audios produced by differential decryption can achieve the expected security levels. Some security tests also yielded excellent results, such as an NSCR value of 1.

With the popularity of cloud servers, an increasing number of people utilize cloud platforms to manage their images. Images uploaded to the cloud are no longer directly controlled by the owner, which has raised concerns among users about privacy leakage. In response, some thumbnail-preserving encryption methods have been proposed to protect image privacy. However, since privacy lacks a clear definition, these methods face challenges in quantifying privacy, making it difficult for users to assess the effectiveness of privacy protection. To address this issue, we propose an image obfuscation method based on novel differential privacy mechanism, which provides provable privacy protection while preserving thumbnails. Additionally, our method can maintain the consistency of image distributions to enhance the utility of protected images. Extensive experimental results demonstrate that our method significantly outperforms existing work in terms of privacy and utility.

With the widespread application of computer vision, the scenarios in terms of visual privacy have become increasingly diverse and meanwhile numerous studies have been conducted to address privacy concerns in these scenarios. However, these studies are individually tailored for specific scenarios, making their layouts challenging to be drawn upon easily. When encountering a new scenario, it takes significant additional efforts to redesign a scheme due to the low referability of previous works. To tackle this issue, we explore commonalities among existing works and propose a generalized framework to meet the demand for visual privacy protection in various scenarios. Our framework is elaborately organized into several crucial steps, including privacy definition, scenario abstraction, algorithm design, and effect evaluation. It serves as a guide for researchers to efficiently design visual privacy protection schemes. In our framework, we establish a unified standard for quantifying privacy and introduce a novel constrained optimization theory to balance privacy and usability, which contributes to a broader understanding of visual privacy protection. Furthermore, we present an instance under the guidance of the framework that can support identity protection and attribute control scenarios through a diffusion-based model. Extensive experimental results demonstrate the effectiveness of our framework.