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 show some representative benchmarks, present a statistical analysis, and summarize the 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转折点。借助AIGC，可以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，学术界与工业界开始重视生成数据的可信度，相继提出了一系列针对安全与隐私的防护措施。本综述系统性地梳理了AIGC中生成数据的安全与隐私问题，首次从信息安全属性的视角对其进行分析。具体而言，我们分别从隐私性、可控性、真实性与合规性这些基础属性出发，揭示了现有先进防护措施的成功经验。最后，我们展示了若干代表性基准测试，提供了统计分析，并从各属性维度总结了潜在的探索方向。