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基于数据特征的电力数据隐私保护模型研究 被引量:1
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作者 张岚 王献军 程勇 《电气自动化》 2022年第6期57-59,62,共4页
为避免发生隐私泄露和数据缺失,研究了基于数据特征的电力数据隐私保护模型。依据不同的数据属性划分原始数据集,形成特征集和候选集后,采用基于最大信息系数的特征分类模型,分析形成的两种数据集,获取最高的相关性形成隐私数据集;通过... 为避免发生隐私泄露和数据缺失,研究了基于数据特征的电力数据隐私保护模型。依据不同的数据属性划分原始数据集,形成特征集和候选集后,采用基于最大信息系数的特征分类模型,分析形成的两种数据集,获取最高的相关性形成隐私数据集;通过差分隐私的数据匿名隐私保护模型,利用差分隐私技术获取隐私保护匿名数据集,完成数据隐私保护。测试结果表明:模型在合理的隐私保护预算范围内,运算性能良好,保护后数据记录连接值低于0.23,可较大程度保证数据的隐私性和可用性,降低数据损失率,应用性较好。 展开更多
关键词 数据特征 电力数据 隐私保护模型 隐私数据 差分隐私 匿名数据集
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Towards a respondent-preferred k_i-anonymity model
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作者 Kok-Seng WONG Myung Ho KIM 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2015年第9期720-731,共12页
Recently, privacy concerns about data collection have received an increasing amount of attention. In data collection process, a data collector (an agency) assumed that all respondents would be comfortable with submi... Recently, privacy concerns about data collection have received an increasing amount of attention. In data collection process, a data collector (an agency) assumed that all respondents would be comfortable with submitting their data if the published data was anonymous. We believe that this assumption is not realistic because the increase in privacy concerns causes some re- spondents to refuse participation or to submit inaccurate data to such agencies. If respondents submit inaccurate data, then the usefulness of the results from analysis of the collected data cannot be guaranteed. Furthermore, we note that the level of anonymity (i.e., k-anonymity) guaranteed by an agency cannot be verified by respondents since they generally do not have access to all of the data that is released. Therefore, we introduce the notion of ki-anonymity, where ki is the level of anonymity preferred by each respondent i. Instead of placing full trust in an agency, our solution increases respondent confidence by allowing each to decide the preferred level of protection. As such, our protocol ensures that respondents achieve their preferred kranonymity during data collection and guarantees that the collected records are genuine and useful for data analysis. 展开更多
关键词 Anonymous data collection Respondent-preferred privacy protection K-ANONYMITY
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