Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 1985-1990.DOI: 10.11772/j.issn.1001-9081.2018122466

• Cyber security • Previous Articles     Next Articles

Privacy protection based on local differential privacy for numerical sensitive data of wearable devices

MA Fangfang, LIU Shubo, XIONG Xingxing, NIU Xiaoguang   

  1. School of Computer Science, Wuhan University, Wuhan Hubei 430072, China
  • Received:2018-12-13 Revised:2019-01-30 Online:2019-07-10 Published:2019-03-29
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (61872431), the Major Technical Innovation Project of Hubei Province (2018AAA046), the Applied Basic Project of Wuhan (2017060201010162).


马方方, 刘树波, 熊星星, 牛晓光   

  1. 武汉大学 计算机学院, 武汉 430072
  • 通讯作者: 刘树波
  • 作者简介:马方方(1993-),女,安徽阜阳人,硕士研究生,主要研究方向:信息安全、差分隐私;刘树波(1970-),男,湖北武汉人,教授,博士生导师,博士,主要研究方向:信息安全、嵌入式系统及安全;熊星星(1989-),男,江西南昌人,博士研究生,主要研究方向:信息安全、差分隐私;牛晓光(1979-),男,河北保定人,副教授,博士,主要研究方向:移动计算、无线传感网、信息安全。
  • 基金资助:



Focusing on the issue that collecting multi-dimensional numerical sensitive data directly from wearable devices may leak users' privacy information when a data server was untrusted, by introducing a local differential privacy model, a personalized local privacy protection scheme for the numerical sensitive data of wearable devices was proposed. Firstly, by setting the privacy budget threshold interval, a users' privacy budget within the interval was set to meet the individual privacy needs, which also met the definition of personalized local differential privacy. Then, security domain was used to normalize the sensitive data. Finally, the Bernoulli distribution was used to perturb multi-dimensional numerical data by grouping, and attribute security domain was used to restore the disturbance results. The theoretical analysis shows that the proposed algorithm meets the personalized local differential privacy. The experimental results demonstrate that the proposed algorithm has lower Max Relative Error (MRE) than that of Harmony algorithm, thus effectively improving the utility of aggregated data collecting from wearable devices with the untrusted data server as well as protecting users' privacy.

Key words: wearable device, untrusted third-party, local differential privacy, personalization, normalization



关键词: 可穿戴设备, 不可信第三方, 本地差分隐私, 个性化, 归一化

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