Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset - 2017 PROJECT TITLE : Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset - 2017 ABSTRACT: Clustering techniques have been widely adopted in several real world knowledge analysis applications, such as client behavior analysis, targeted promoting, digital forensics, etc. With the explosion of knowledge in today’s massive knowledge era, a significant trend to handle a clustering over massive-scale datasets is outsourcing it to public cloud platforms. This is because Cloud Computing offers not solely reliable services with performance guarantees, but conjointly savings on in-house IT infrastructures. However, as datasets used for clustering could contain sensitive information, e.g., patient health information, business knowledge, and behavioral knowledge, etc, directly outsourcing them to public cloud servers inevitably raise privacy considerations. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest PPHOPCM: Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering with Cloud Computing - 2017 Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid - 2017
