Efficient and Secure Quantile Aggregation of Private Data Streams

ePrint Report: Efficient and Secure Quantile Aggregation of Private Data Streams

Xiao Lan, Hongjian Jin, Hui Guo, Xiao Wang

Computing the quantile of a massive data stream has been a crucial task in networking and data management. However, existing solutions assume a centralized model where one data owner has access to all data. In this paper, we put forward a study of secure quantile aggregation between private data streams, where data streams owned by different parties would like to obtain …

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