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.

中文翻译：

---

AIGC 中生成数据的安全性和隐私性：一项调查


人工智能生成内容 （AIGC） 的出现代表了信息技术发展的关键时刻。借助 AIGC，可以毫不费力地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中的扩散带来了安全和隐私问题，包括个人隐私泄露和用于欺诈目的的媒体伪造。因此，学术界和工业界都开始强调生成数据的可信度，相继为安全和隐私提供了一系列对策。在这项调查中，我们系统地审查了 AIGC 中生成数据的安全性和隐私性，特别是首次从信息安全属性的角度对其进行分析。具体来说，我们分别从隐私、可控性、真实性和合规性等基本属性方面揭示了最先进的对策的成功经验。最后，我们展示了一些具有代表性的基准，提出了一个统计分析，并总结了每个项目区的潜在勘探方向。