摘要
智慧城市利用先进的信息技术和通信技术实现城市智能的高效运行,在运行过程中会产生海量多源异构数据。然而,这些数据通常包含大量的个人或组织的敏感信息,在采集过程中面临着严重的隐私泄露风险。此外,城市数据具有动态性强、增长迅速、实时性强等特点,使得传统的数据采集隐私保护方法不再适用。针对城市大数据的特点,采用(a,k)–匿名模型作为数据采集隐私保护方案,利用分布式框架MapReduce对海量、动态数据集进行处理,提出了一种适用于大规模动态环境下的城市大数据采集隐私保护方案。实验结果和理论分析表明,所提方案不仅能有效保护数据隐私,还具有减少信息损失量和降低执行时间的特点。
Smart city use advanced information technology and communication technology to achieve efficient operation of urban intelligence, which generates massive multi-source heterogeneous data during its operation. However, this data usually contains a large amount of sensitive information of individuals or organizations, which makes the data facing a serious risk of privacy leakage during the collection process. In addition, urban data has the characteristics of strong dynamics, rapid growth, and strong real-time performance, making traditional data collection privacy protection methods no longer applicable. In view of the characteristics of urban big data, the (a,k)-anonymous model was adopteras the data collection privacy protection scheme, and the distributed framework MapReduce was used to process massive and dynamic data sets, and a city big data collection privacy suitable protection plan for large-scale dynamic environment was proposed. The experimental results and theoretical analysis show that the scheme not only can effectively protect data privacy, but also has the characteristics of small information loss and low execution time.
引文
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