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一个关键时刻。借助AIGC，能够轻松生成公众难以区分的高质量数据。然而，生成数据在网络空间的广泛传播带来了安全与隐私问题，包括个人隐私泄露和用于欺诈目的的媒体伪造。因此，学术界和工业界开始强调生成数据的可信性，相继提供了一系列针对安全与隐私的对策。在本综述中，我们系统性地回顾了AIGC中生成数据的安全与隐私问题，特别是首次从信息安全属性的角度对其进行分析。具体而言，我们从隐私、可控性、真实性和合规性这四个基本属性层面，揭示了当前最先进对策的成功经验。最后，我们总结了每个属性所面临的开放挑战及潜在的探索方向。