面向在线生成式人工智能服务的隐私保护方法OA

In recent years,state-of-the-art online artificial intelligence systems demonstrate remarkable capabilities in various fields,exerting broad social impacts.In order to access these model services,users are typically required to upload their personal data to the cloud platform.However,these queries may contain sensitive or confidential information,and di-rectly sharing them with cloud platforms introduces potential privacy leakage risks.Moreover,platforms may exploit user data for further model training,causing private information to be memorized by the model and later regenerated in public services,thereby aggravating the risk of privacy breaches.Existing privacy-preserving mechanisms in generative AI applica-tions predominantly rely on prompt sanitization techniques,whose security critically depends on the accuracy of sensitive information identification.These approaches usually require large amounts of annotated data for model training,which not only raises implementation costs but may also introduce new privacy vulnerabilities in specific scenarios.To address this is-sue,this paper proposes a novel privacy-preserving collaborative learning framework named PrivateAI.The core idea of this framework is to fully exploit sensitive data distributed across different devices to train local privacy identification mod-els,while strictly ensuring data privacy.Meanwhile,PrivateAI extracts the implicit knowledge embedded in the large foun-dation models and compresses it into a lightweight distilled dataset,thereby achieving effective privacy detection perfor-mance enhancement of local models.In addition,to tackle the heterogeneity challenge between the knowledge extracted from labeled data and foundation models,the framework introduces a heterogeneous knowledge fusion mechanism that aligns and integrates multi-source knowledge from both the foundational models and distributed labeled datasets.We evalu-ate PrivateAI on two datasets,and the results demonstrate that models learned by PrivateAI can maximally improve the pri-vacy protection success rate by 53.7 percentage points.PrivateAI holds significant potential in mitigating privacy breaches,acting as a sentinel against severe privacy leakage incidents within online AI applications.