多模态学习中的隐私关联泄露模型与保护机制研究

多模态学习技术的快速发展打破了图像、音频、文本等不同模态之间的数据壁垒，使得单一模态下非敏感的信息也可能被关联分析出隐式隐私信息，加剧了用户隐私泄露的风险。与此同时，精准分析服务需求与用户隐私保护意识之间的矛盾日益凸显，已成为阻碍大数据行业健康有序发展的瓶颈。为此，全面分析并抽象大数据环境下不同模态数据之间的内在联系，聚焦隐式隐私信息关联泄露模型构建与风险阻断、兼顾隐私保护与价值挖掘的多模态数据融合两个关键科学问题，重点研究多模态数据隐私信息甄别与关联标识定位、多方利益均衡的异构数据隐私协同保护、服务质量最大化的隐私模型参数优化三个方面内容，从而及时发现并有效预防多来源多类型数据的隐私关联泄露风险，补齐不同模态数据隐私保护能力短板，为推动用户隐私保护和应用服务质量的长效均衡发展提供有益探讨。

The rapid development of multimodal has broken down the barriers among the various modal data, such as images, audio, and text. Hence, more implicit private information can be deduced from formerly public non-sensitive information, which further aggravates the threat of privacy disclosure. Besides, the contradiction between precise analysis service requirements and user privacy-preserving awareness has become increasingly prominent. It has become a bottleneck that hinders the healthy and orderly development of the big data industry. For this reason, it is necessary to comprehensively analyze and abstract the internal relationship between different modal data, focus on the two critical scientific issues. (1) association leakage model construction and risk blocking of implicit privacy information; (2) the balance of privacy protection and service quality in multimodal data fusion. The highlights of this project are the recognition of multimodal data privacy and positioning of association identification, the privacy coordinating protection for heterogeneous data with benefit equilibrium, and the optimization of privacy model parameters for maximum service quality. Then timely detection and effective prevention for the private association disclosure from multi-source multi-type data can be achieved. This research can also be used to make up the shortcomings of different modal data privacy-preserving capabilities, and promote the balanced development between privacy protection and quality of service in the long term.