Research on Privacy Security Risk Evaluation of Intelligent Recommendation Mobile Applications Based on a Hierarchical Risk Factor Set

Intelligent recommendation applications based on data mining have appeared as prospective solution for consumer's demand recognition in large-scale data, and it has contained a great deal of consumer data, which become the most valuable wealth of application providers. However, the increasing threat to consumer privacy security in intelligent recommendation mobile application (IR App) makes it necessary to have a risk evaluation to narrow the gap between consumers' need for convenience with efficiency and need for privacy security. For the previous risk evaluation researches mainly focus on the network security or information security for a single work, few of which consider the whole data lifecycle oriented privacy security risk evaluation, especially for IR App. In this paper, we analyze the IR App's features based on the survey on both algorithm research and market prospect, then provide a hierarchical factor set based privacy security risk evaluation method, which includes whole data lifecycle factors in different layers.