Instruction and demonstration-based secure service attribute generation mechanism for textual data

Attribute-based access control is fundamentally dependent on the secure service attribute calibration of object sources. Traditionally, the calibration of secure service attribute for textual data has been primarily reliant on human experts and machine learning methods, yet the efficiency and few-shot ability are insufficient. Moreover, traditional approaches have predominantly utilized entities in textual data as service attributes, resulting in coarse granularity, uncontrollable scale and management level, further leading to the problem of attribute-space explosion. Thus, a secure service attribute generation mechanism for textual data (IDSAM) was introduced. This mechanism addressed the aforementioned challenges by transforming the extraction of candidate service attributes, previously a sequence-calibrated problem, into a controllable-generation problem through instruction learning and in-context learning. Subsequently, WordNet was employed to achieve semantic deduplication and generalization of the candidate service attributes. Concurrently, to prevent semantic loss due to over-generalization, a cosine similarity threshold was regulated, enabling the generation of a service attribute set. Finally, a weighted directed acyclic attribute graph was constructed based on the similarity between initial and derived attributes within the set, facilitating the dynamic construction of a secure service attribute library with a controllable scale and adjustable security granularity, in accordance with security control requirements. The candidate service attribute extraction component of the proposed mechanism achieves an optimal average <italic>F</italic>1 score in few-shot experiments on the CoNLL-2003 dataset, surpassing the baseline model. This positions the mechanism as state-of-the-art. Furthermore, the mechanism is capable of dynamically mining secure service attributes with adjustable security control levels and controllable scales to meet varying security management requirements. The experimental results indicate that the proposed mechanism is effective in generating secure service attributes with the desired characteristics.