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

LI Chenhao; WANG Na; LIU Aodi

doi:10.11959/j.issn.2096-109x.2024082

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

Papers | 更新时间：2025-02-06

- Instruction and demonstration-based secure service attribute generation mechanism for textual data
- Chinese Journal of Network and Information Security Vol. 10, Issue 6, Pages: 81-95(2024)
- 作者机构：
  
  信息工程大学，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The Natural Science Foundation of China(61802436;62102449)
- DOI：10.11959/j.issn.2096-109x.2024082
  CLC： TP391
- Received：29 May 2024，
  
  Revised：2024-08-12，
  
  Published：25 December 2024
- 稿件说明：
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李晨皓, 王娜, 刘敖迪. 基于指令与描述的文本数据安全业务属性生成机制[J]. 网络与信息安全学报, 2024, 10(6): 81-95.

LI C H, WANG N, LIU A D. Instruction and demonstration-based secure service attribute generation mechanism for textual data[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 81-95.
李晨皓, 王娜, 刘敖迪. 基于指令与描述的文本数据安全业务属性生成机制[J]. 网络与信息安全学报, 2024, 10(6): 81-95. DOI： 10.11959/j.issn.2096-109x.2024082.

LI C H, WANG N, LIU A D. Instruction and demonstration-based secure service attribute generation mechanism for textual data[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 81-95. DOI： 10.11959/j.issn.2096-109x.2024082.

摘要

客体资源的安全业务属性标定是实施基于属性的访问控制机制的基础和关键。传统上，文本数据安全业务属性标定主要依靠人类专家及机器学习的方法，其效率及少样本能力过于低，并且传统方法多以文本数据中的实体作为业务属性，所提取的属性粒度粗糙、规模及安全管控层级均不可控，极易导致属性空间膨胀问题。基于此，提出了一种基于指令与描述的文本数据安全业务属性生成机制（IDSAM

instruction and demonstration-based secure service attribute generation mechanism for textual data）。具体来说，首先将安全业务属性生成中的候选业务属性（即实体）提取任务由序列标定问题转换为可控生成问题，利用指令学习和情境学习技术实现对候选业务属性的提取；然后，利用WordNet实现对候选业务属性的语义泛化及去冗余，同时通过控制余弦相似度阈值的方法避免语义过于泛化带来的语义丢失问题，生成业务属性集；最后，以集合中初始属性与衍生属性间相似度为边权重，构建加权有向无环属性图，实现了能够根据安全管控需求动态生成、规模可控、安全粒度可调的安全业务属性库构建。实验结果表明，所提机制中候选业务属性提取部分功能在CoNLL-2003数据集上的少样本实验中取得的平均

1分数较基线模型更高，并且所提机制能够按照安全管控需求，动态挖掘出安全管控层级可调、规模可控的安全业务属性。

Abstract

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

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.

关键词

Keywords

references

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