Differentially private top-k query over MapReduce
Han Xu, Miao Wang, Zhenjie Zhang, Xiaofeng Meng · 2012
Discovering that Map-Reduce framework is a popular way to deal with a large scale of data, but there is a significant risk to leak out users' personal information, especially when the data is sensitive, for example, including users' health records, salary information, etc. Differential privacy has recently emerged as a new paradigm for preserving private data. This makes it possible to provide strong theoretical guarantees on the privacy and utility of the query results. In this paper, we focus on top-k query which is one of the most useful queries in Map-Reduce framework over big data sets.