The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we summarize the open challenges and potential exploration directions from each of theses properties.

翻译：人工智能生成内容（AIGC）的出现标志着信息技术发展的关键时刻。借助AIGC，可以轻松生成高质量数据，且公众难以辨别其真伪。然而，生成数据在网络空间的广泛扩散引发了安全与隐私问题，包括个人隐私泄露以及用于欺诈目的的媒体伪造。因此，学术界与工业界开始重视生成数据的可信性，并陆续提出一系列安全与隐私对策。本综述系统梳理了AIGC中生成数据的安全与隐私问题，特别首次从信息安全属性的角度进行分析。具体而言，我们分别从隐私性、可控性、真实性与合规性等基础属性层面揭示了现有先进对策的成功经验。最后，我们总结了各类属性面临的公开挑战与潜在探索方向。