PROJECT TITLE :

Practical Privacy-Preserving MapReduce Based Kmeans Clustering over Large-scale Dataset - 2017

ABSTRACT:

Clustering techniques have been widely adopted in many universe data analysis applications, like client behavior analysis, targeted promoting, digital forensics, etc. With the explosion of knowledge in nowadays’s massive information era, a major trend to handle a clustering over giant-scale datasets is outsourcing it to public cloud platforms. This is because cloud computing offers not solely reliable services with performance guarantees, however conjointly savings on in-house IT infrastructures. However, as datasets used for clustering might contain sensitive data, e.g., patient health data, business data, and behavioral data, etc, directly outsourcing them to public cloud servers inevitably raise privacy concerns.


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