Enhancing privacy in participatory sensing applications with multidimensional data
Enhancing privacy in participatory sensing applications with multidimensional data
复制标题
DOI：
10.1109/percom.2012.6199861
复制
发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Michael M. Groat;Benjamin Edwards;James L. Horey;Wenbo He;S. Forrest
中科院分区：
文献类型：
--
作者：
Michael M. Groat;Benjamin Edwards;James L. Horey;Wenbo He;S. Forrest
参与式传感应用依靠个人与他人共享本地和个人数据来生成聚合模型和知识。在这种情况下，隐私是一个重要的考虑因素，缺乏隐私可能会阻碍许多令人兴奋的应用程序的广泛采用。我们提出了一种使用负面调查的多维数据的隐私保护参与感知方案。多维数据（例如包含位置和环境字段的属性向量）对于隐私保护来说具有挑战性，并且在参与式传感应用中很常见。当在负面调查中报告数据时，个体参与者从感测数据值的集合补充中随机选择一个值，每个维度一次，并将负面值返回到中央收集服务器。使用本文描述的算法，服务器可以重建感测值原始分布的概率密度函数，而无需知道参与者的实际数据。我们的算法避免了计算成本高昂的加密和密钥管理方案，从而节省了能源。我们研究准确性和隐私之间的权衡，以及它们与维度、类别和参与者数量的关系。我们引入了尺寸调整，这是一种减少与早期工作相关的误差放大的方法。两个模拟场景说明了该方法如何保护参与者多维数据的隐私，同时允许收集有用的聚合信息。
Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, and lack of privacy could discourage widespread adoption of many exciting applications. We present a privacy-preserving participatory sensing scheme for multidimensional data which uses negative surveys. Multidimensional data, such as vectors of attributes that include location and environment fields, are challenging for privacy protection and are common in participatory sensing applications. When reporting data in a negative survey, an individual participant randomly selects a value from the set complement of the sensed data value, once for each dimension, and returns the negative values to a central collection server. Using algorithms described in this paper, the server can reconstruct the probability density functions of the original distributions of sensed values, without knowing the participants' actual data. Our algorithms avoid computationally expensive encryption and key management schemes, conserving energy. We study trade-offs between accuracy and privacy, and their relationships to the number of dimensions, categories, and participants. We introduce dimensional adjustment, a method that reduces the magnification of error associated with earlier work. Two simulation scenarios illustrate how the approach can protect the privacy of a participant's multidimensional data while allowing useful aggregate information to be collected.