社交网络用户属性的隐私度量与决策分析方法  

Privacy Measurement and Decision Analysis for User Attributes on Social Network

作　　者：陈希 叶帼华[2] 宫宇宏 CHEN Xi;YE Guohua;GONG Yuhong(College of Information Engineering,Fujian Business University,Fuzhou 350506,China;College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China)

机构地区：[1]福建商学院信息工程学院,福建福州350506 [2]福建师范大学计算机与网络空间安全学院,福建福州350117

出　　处：《福建师范大学学报（自然科学版）》2024年第2期90-96,共7页Journal of Fujian Normal University：Natural Science Edition

基　　金：国家自然科学基金资助项目(62272103)。

摘　　要：提出一种基于模糊层次分析和逼近理想解排序法的隐私决策优化方法,为用户在社交网络场景中实施隐私决策时提供可靠的隐私建议。首先,将社交属性的敏感度、独特性效用和普遍性效用作为价值评价指标,给出相应的量化方法,评价指标的权重计算过程是根据评价指标重要性的评价规则,采用模糊层次分析法构造模糊判断矩阵。然后,采用逼近理想解排序法对社交属性进行综合评估分析和排序,通过实验分析比较3种评价规则的个性化评估结果,实验结果表明该方法在满足个性化隐私需求的前提下能指导用户优化个人信息配置。This paper proposes an optimized privacy decision-making scheme based on fuzzy analytic hierarchy process and the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),offering reliable privacy recommendations for users in social network scenarios.Initially,the sensitivity,uniqueness utility,and universality utility of social attributes are considered as value evaluation indicators,with a specific quantification method provided.The weights of these evaluation indicators are determined by constructing a fuzzy judgment matrix using the fuzzy analytic hierarchy process,according to pre-defined evaluation rules.Subsequently,the technique for order preference by similarity to ideal solution is applied to perform a comprehensive evaluation and ranking of social attributes.Experimental analysis comparing personalized evaluation results across three evaluation rules demonstrates that the proposed method can guide users to optimize their personal attribute settings while meeting individualized privacy needs.

关 键 词：社交网络 隐私度量 模糊层次分析 敏感度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]