Information Security Precautions in the AIGC Era

With the rapid development of generative artificial intelligence (AIGC) technology, we are stepping into a brand-new era.  The core of this technology lies in the continuous refinement of deep learning and neural network algorithms, which is promoting technological innovation and diversified applications in the industrial sector.  In multiple crucial domains such as education, healthcare, entertainment, and finance, AIGC technology is demonstrating its extensive application prospects.

The utilization of AIGC technology in different industries pertains to all aspects of users.  Many of these diverse applications are premised on collecting or processing various personal information of users, which brings about various issues for users, such as improper use of personal information, dissemination of false information, and leakage of user privacy.  The AIGC era has presented new challenges for the protection of personal information.

Regarding the problems arising from the selection of training data, relevant industry norms or management regulations should be established to clarify the available scope and extent of information.  China’s “Interim Measures for the Administration of Generative Artificial Intelligence Services” stipulates that the model training data should have legal sources, not infringe upon others’ intellectual property rights, and personal information should be used in accordance with the law.  In the specific implementation process, the usage limits of data from different channels can be further classified and regulated.  Additionally, research and development of data desensitization and sensitive data identification technologies can be strengthened to expand the development space of large models as much as possible while ensuring the information security of individuals and the country.  For fairness issues caused by improper data selection, annotation, or weak algorithms, it is not only necessary for the R & D side to clearly define annotation norms but also to enhance the development and application of technologies such as Reinforcement Learning from Human Feedback (RLHF), Reinforcement Learning from AI Feedback (RLAIF), and In-Context Learning (ICL) to continuously correct deviations technologically.

Strengthen data protection and management: Ensure the legal acquisition and storage of data, strengthen data encryption and access control mechanisms, and prevent unauthorized access and abuse.

Introduce privacy protection technologies: Introduce privacy protection technologies such as differential privacy and homomorphic encryption in the process of algorithm design and model training to reduce the risk of privacy leakage.

Strengthen supervision and self-discipline: Policy-makers and enterprises should strengthen supervision and self-discipline, formulate strict privacy protection policies and standards, and promote the healthy development of AIGC technology.

For the problem of excessive acquisition and retention of user information and contextual information, efforts should be made simultaneously from both the regulatory and development ends.  On the one hand, the depth of information acquisition should be stipulated according to the type and source of AIGC applications;  on the other hand, service providers are required to improve the transparency of user information policies to ensure that users understand the extent and destination of information collection.  Technically, explore the ideas of data desensitization and data deletion verification of large models to effectively regulate information retention.  For relevant management departments, first, the application scope of AIGC technology should be strictly regulated and accountability regulations should be formulated.  Secondly, the accuracy and sensitivity of pre-warning technologies should be improved, and data should be encrypted and identified through technologies such as digital watermarking to prevent the leakage of sensitive information technologically.

For the improper information generation of AIGC, in addition to supervising the data algorithm, the international standards and industry norms for AIGC identification should also be improved as soon as possible.  The boundary between AIGC and human-generated content should be established from technical rules to prevent the abuse of AIGC.  For relevant management departments, they should strictly prevent the theft of secrets and subversion using AIGC technology.  They should not only enhance the information security awareness of staff through publicity and education but also pay timely attention to the attack effects of AIGC technology on security measures such as biometric recognition and take preventive measures.

AIGC technology now has the ability to produce content that is highly similar to what humans create, whether text, images, audio or video, with a degree of realism that makes it difficult to discern authenticity. This highly simulated and efficient output indicates that the technology will bring revolutionary changes to various industries. In the field of education, the automatic generation of personalized learning materials is possible; In the medical field, the realization of precision medical assistance is a step closer; In entertainment and finance, AIGC technology is leading an unprecedented wave of innovation.