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Privacy-preserving comparison based data aggregation protocols for mobile sensing
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AI
DOI：10.1007/s12083-021-01264-6.png)
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Abstract
En 中文
Aggregation statistics in mobile sensing are very useful for many applications. Therefore, a large number of privacy-preserving data aggregation protocols have been proposed, most of which however, only consider privacy-preserving sum aggregation. Non-additive aggregation statistics are also very important for their widely applications, such as max/min value and variance. The max/min aggregation can be regarded as a kind of comparison based aggregation because to obtain the max/min value, comparison among sensed data is required. Few protocols takes comparison based aggregation into consideration for the reason that it is difficult to implement privacy-preserving comparison. In this paper, we present two methods, i.e., a distributed one and a centralized one, for comparing all the sensed data in a privacy-preserving manner. And based on the two comparison methods, we propose two privacy-preserving comparison based data aggregation protocols, respectively, which are further applied to achieve max/min aggregation and top X aggregation.
Keywords:
Privacy-Preserving
Comparison based aggregation
Max
Min aggregation
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