Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs in OSN image sharing. However, OSN image privacy itself is quite complicated, and solutions currently in place for privacy management in reality are insufficient to provide personalized, accurate and flexible privacy protection. A more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a survey of "privacy intelligence" that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present a definition and a taxonomy of OSN image privacy, and a high-level privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and the privacy issues associated with these behaviors. Then a systematic review on the representative intelligent solutions targeting those privacy issues is conducted, also in a stage-based manner. The resulting analysis describes an intelligent privacy firewall for closed-loop privacy management. We also discuss the challenges and future directions in this area.

翻译：在线社交网络（OSNs）上的图像分享已成为日常社交活动中不可或缺的一部分，但这也导致了隐私侵犯风险的增加。近期流行OSN服务中发生的图像泄露事件，以及利用先进算法（如DeepFake）对个人照片的滥用，促使公众重新思考OSN图像分享中的个人隐私需求。然而，OSN图像隐私本身相当复杂，当前现实中用于隐私管理的解决方案不足以提供个性化、准确且灵活的隐私保护。一个更智能的隐私友好型OSN图像分享环境亟待构建。为填补这一空白，我们从用户中心视角出发，贡献了一篇针对动态OSN图像分享中现代隐私问题的“隐私智能”综述。具体而言，我们提出了OSN图像隐私的定义与分类，并基于OSN图像分享的生命周期构建了一个高层隐私分析框架。该框架包含三阶段，每阶段遵循不同的“设计即隐私”原则。在每个阶段，我们识别了OSN图像分享中的典型用户行为及其相关的隐私问题。随后，我们以分阶段方式系统综述了针对这些隐私问题的代表性智能解决方案。由此得到的分析描述了一个用于闭环隐私管理的智能隐私防火墙。最后，我们讨论了该领域的挑战与未来方向。