Collaborative Research: SaTC: CORE: Medium: Broad-Spectrum Facial Image Protection with Provable Privacy Guarantees

随着智能电话和其他移动的设备的日益普及，图像共享在社交网络中越来越流行。隐私保护现在已经成为一个需要解决的关键问题，因为共享图像可以揭示个人和社会环境以及私人生活的细节。虽然许多社交媒体允许用户设置隐私偏好，但由于选项的限制，问题的复杂性以及隐私配置的繁琐性，这些很少是足够的。该项目的目标是设计一个智能和自动的广谱图像保护系统，为参与社交图像共享的多方提供可证明的隐私保证，同时保持图像质量。具体而言，该项目的研究人员旨在克服以下挑战：（i）对图像背景中的人类主体和敏感物体的隐私保护;（ii）考虑位置相关的图像敏感性，即在某些地方拍摄的图像（例如，（iii）严格执行隐私保护，以符合同一图像中多个人的不同隐私需求。这项研究的成功将解决社交网站上越来越多的图像共享隐私问题，并使数十亿社交网络用户受益。该项目还将开展一系列教育活动，包括课程开发、大学生专业培训和K-12教师和学生的外展，重点关注代表性不足的群体。该项目将通过以下创新的研究思路，大大推进在线图像共享过程中最先进的面部隐私保护。首先，一个新的图像隐私策略语言和一个有效的策略管理系统将被设计用于管理多个方面的广谱隐私关注。其次，将定义正式的隐私模型来量化隐私风险，并在政策执行期间提供可证明的隐私保证。第三，将研究基于深度学习的新图像修改方法，如面部修改/替换和图像裁剪，以同时满足不同用户对同一图像的隐私需求，同时保留美学性质。最后，将结合界面和激励设计，以获得更准确的用户反馈，并评估拟议系统的有效性和实用性。该奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

With the growing ubiquity of smartphones and other mobile devices, image sharing is gaining increasing popularity in social networks. Privacy protection has now become a crucial issue to be addressed because sharing images can reveal personal and social environments and details of private lives. While many social media allow users to set privacy preferences, these are rarely sufficient due to the limitations of the options, complexity of the problem, and the tedious nature of privacy configuration. The objective of this project is to design an intelligent and automatic broad-spectrum image protection system that offers provable privacy guarantees for multiple parties involved in social image sharing while maintaining image quality. Specifically, the researchers on this project aim to overcome the following challenges: (i) Privacy protection for human subjects and sensitive objects in the background of the images; (ii) Consideration of location-dependent image sensitivities whereby images taken at certain places (e.g., pubs, hospitals) may impact privacy, such as people in the images who do not want their occurrences or co-occurrences at those locations to be known; (iii) Strong enforcement of the privacy protection that conforms with different privacy needs of multiple people in the same image. The success of the proposed research will address the rising privacy concerns of image sharing on social sites and benefit billions of social network users. A range of educational activities will be also carried out including curriculum development, professional training for college students and outreach to K-12 teachers and students, with emphasis on under-represented groups.This project will greatly advance the state-of-the-art facial privacy protection during online image sharing with the following innovative research ideas. First, a new image privacy policy language and an efficient policy management system will be designed for managing broad-spectrum privacy concerns of multiple parties. Second, formal privacy models will be defined to quantify privacy risks and provide provable privacy guarantees during policy enforcement. Third, new deep-learning-based image modification approaches such as facial modification/replacement and image cropping will be investigated to simultaneously address different users' privacy needs regarding the same image while preserving the aesthetics nature. Finally, a combination of interface and incentive design will be conducted to obtain more accurate user feedback and evaluate the effectiveness and practicality of the proposed system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.