摘要
[目的/意义]从移动医疗服务的信息生命周期视角,分析患者和移动医疗APP服务商的隐私问责与赔偿行为,有效引导与促进隐私保护。[方法/过程]设计由患者和移动医疗APP服务商组成的博弈参与主体,研究了博弈双方的隐私问责与赔偿的行为策略,依据不同行为策略的支付矩阵,建立了患者与APP服务商的演化博弈模型,并分析得到隐私问责与赔偿的演化稳定策略。[结果 /结论]结果表明,患者与APP服务商的隐私策略与其问责成本、赔偿金额、监管力度及隐私损失等因素密切相关,处于不同参数条件时,会出现多种演化稳定策略。运用Matlab进行了数值仿真,验证了模型的有效性,并给出了相关对策和建议。
[Purpose/Significance]From the perspective of information life cycle of mobile health(mHealth)service,this paper tried to analyze the accountability and compensation behavior of mHealth service providers and patients that can guide and promote efficient privacy protection.[Method/Process]This paper designed the game agents consisted of mHealth service providers and patients,and investigated the strategies of privacy accountability and compensation.According to the different payoff matrices of game players when adopting different strategies,this paper developed an evolutionary game model and proposed evolutionary stable strategies of their privacy behavior.[Result/Conclusion]The results showed that the privacy accountability and compensation strategies of mHealth service providers and patients were related to the accountability costs,compensation earnings,and regulation effect and privacy loss.Different evolutionary stable strategies could be found when the related parameters change.At last,a numerical verification for the mathematical model was given and several countermeasures and suggestions were proposed for the development and implementation of privacy protection.
作者
朱光
刘虎
陈婧
杜欣蒙
Zhu Guang;Liu Hu;Chen Jing;Du Xinmeng(School of Management Science and Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;China Institute of Manufacturing Development,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《现代情报》
CSSCI
2018年第12期32-39,共8页
Journal of Modern Information
基金
国家自然科学基金项目"信息生命周期视角下的大数据隐私风险评估和溯源问责机制研究"(项目编号:71503133)
国家社会科学基金重大项目"中国社会应急救援服务体系建设研究"(项目编号:16ZDA054)
江苏高校品牌专业建设工程资助项目
江苏高校优势学科建设工程资助项目
关键词
移动医疗
APP
隐私问责
损失
成本
演化博弈
mobile health
APP
privacy accountability
loss
cost
evolutionary game
