A Scalable K-Anonymization Solution for Preserving Privacy in an Aging-in-Place Welfare Intercloud

Abstract

Aging-in-Place solutions are becoming increasingly prevalent in our society. New age big data technologies can harness upon enormous amount of data generated from sensors in smart homes to provide enabling services. Added care and preventive services can be furnished through interoperability and bidirectional dataflow across the value chain. However the nature of the problem domain which although allows establishing better care through sharing of information also risks disclosing complete living behavior of individuals. In this paper, we introduce and evaluate a novel scalable k-anonymization solution based upon the distributed map-reduce paradigm for preserving privacy of the shared data in a welfare intercloud. Our evaluation benchmarks both information loss and data quality metrics and demonstrates better scalability/performance than any other available solutions.