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针对当前大数据隐私保护机制的局限性,提出一种社交网络隐私风险的模糊评估方法,引导对大数据隐私提供主动保护。在基于文献研究和专家访谈的基础上,定性分析大数据环境下社交网络的隐私风险因素,运用Delphi法构建包含3个准则维度和13个指标维度的隐私风险评估指标体系,采用层次分析法(AHP)和熵值法计算对各级指标的综合权重。通过问卷调查收集数据,对某社交网络的隐私风险进行模糊评估。实证分析结果表明,该社交网络平台的隐私风险等级处于较低风险,“管理制度”、“第三方信息搜集”、“隐私非法交易”等风险因素处于高风险。综合来看,隐私风险的模糊评估方法具有较好的适用性,可以为提升大数据环境下的社交网络服务水平提供借鉴。
Aiming at the limitations of current big data privacy protection mechanism, this paper proposes a fuzzy evaluation method for privacy risks of social networks, and provides active protection for big data privacy. Based on the literature research and expert interviews, the paper analyzes the privacy risk factors of social networks in big data environment qualitatively and constructs a privacy risk assessment index system containing three criteria dimensions and 13 index dimensions using Delphi method. The analytic hierarchy process AHP) and entropy method to calculate the comprehensive weight of indicators at all levels. Collect data through questionnaires to assess the privacy risks of a social network. The empirical analysis shows that the privacy risk level of the social networking platform is at a relatively low risk, and the risk factors such as “management system”, “third party information collection”, “private illegal trading” are at high risk. Taken together, the fuzzy assessment method of privacy risk has good applicability and can provide reference for improving the social network service in big data environment.