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针对个人信息过度收集风险量化误差较大、告警等级划分精度低等问题,提出APP形式化程序个人信息过度收集风险告警算法研究。获取APP多源异构数据(静态数据、动态行为数据等),通过政策文本的形式化解析、权限信息类型映射与动态行为矢量化等环节将其转化为初始特征空间,提取APP形式化程序多维度特征,以此为基础,计算个人信息过度收集风险因子——个人信息敏感度、个人信息收集行为异常度与个人信息收集行为合规偏离度,通过广义线性模型与Sigmoid函数计算个人信息过度收集风险量化结果,依据个人信息过度收集风险量化结果分布情况,确定自适应阈值,制定个人信息过度收集风险分级告警机制,从而实现研究目标。实验结果显示:设计算法提取APP形式化程序多维度特征向量与实际特征向量的余弦相似度最大值达到了0.95,个人信息过度收集风险量化误差最小值达到了1%,个人信息过度收集风险告警等级划分结果与实验样本告警等级标注结果相同。
Abstract：To address the issues of large quantization errors in personal information over-collection risk and low precision in alarm level classification, this study proposes a formal program personal information over-collection risk alarm algorithm for APPs. Multi-source heterogeneous data of APPs(static data, dynamic behavior data, etc.) are obtained and transformed into an initial feature space through formal parsing of policy texts, permission-information type mapping, and dynamic behavior vectorization. Multi-dimensional features of APP formal programs are extracted, based on which the personal information over-collection risk factors are calculated-personal information sensitivity, personal information collection behavior abnormality, and personal information collection behavior compliance deviation. The personal information over-collection risk quantization result is calculated through a generalized linear model and Sigmoid function. According to the distribution of personal information overcollection risk quantization results, adaptive thresholds are determined and a graded alarm mechanism for personal information over-collection risk is established, thereby achieving the research objectives. Experimental results show that the maximum cosine similarity between the multi-dimensional feature vectors of APP formal programs extracted by the designed algorithm and the actual feature vectors reached 0.95, the minimum quantization error of personal information over-collection risk reached 1%, and the personal information over-collection risk alarm level classification results were identical to the annotated alarm level results of the experimental samples.
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基本信息:
DOI：10.27024/j.wlygc.2025.10.27.04
中图分类号:TP309
引用信息:
[1]宗锐,徐建.APP形式化程序个人信息过度收集风险告警算法[J].物理与工程().DOI:10.27024/j.wlygc.2025.10.27.04.
基金信息:
陕西省职业技术教育学会2025年度职业教育教学改革研究课题“《Java语言程序设计》课程思政案例库建设研究”(编号:2025SZX794); 陕西省教育厅2023年度自然科学一般专项科学研究计划项目“构建基于深度学习的舌体超声影像语言发音模型”(编号:23JK0329)
2026-07-24
2026-07-24
2026-07-24