基于机器遗忘的模型能力细粒度访问控制机制OA

A fine-grained model capability access control mechanism,named Model-Guard,was proposed to address the lack of capability access control in deployed artificial intelligence models,which may lead to unauthorized misuse of model capabilities.Without retraining,sensitive task-related parameters were identified by the selective synaptic dampen-ing(SSD)algorithm and attenuated to disable sensitive capabilities by default.An authorization factor calculation method was designed to restore model capabilities for authorized users.To ensure secure distribution of authorization fac-tors,a hybrid scheme combining symmetric encryption and ciphertext-policy attribute-based encryption(CP-ABE)was adopted,and a Bloom filter was introduced to reduce verification overhead.Experimental results demonstrated that Model-Guard achieved precise capability isolation and restoration in image recognition tasks.The proposed mechanism significantly reduces deployment and maintenance costs while enabling fine-grained and secure capability control.