  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.
