TPII:tracking personally identifiable information via user behaviors in HTTP traffic

It is widely common that mobile applications collect non-critical personally identifiable information(PII)from users'devices to the cloud by application service providers(ASPs)in a positive manner to provide precise and recommending services.Meanwhile,Internet service providers(ISPs)or local network providers also have strong requirements to collect PIIs for finer-grained traffic control and security services.However,it is a challenge to locate PIIs accurately in the massive data of network traffic just like looking a needle in a haystack.In this paper,we address this challenge by presenting an efficient and light-weight approach,namely TPII,which can locate and track PIIs from the HTTP layer rebuilt from raw network traffics.This approach only collects three features from HTTP fields as users'behaviors and then establishes a tree-based decision model to dig PIIs efficiently and accurately.Without any priori knowledge,TPII can identify any types of PIIs from any mobile applications,which has a broad vision of applications.We evaluate the proposed approach on a real dataset collected from a campus network with more than 13k users.The experimental results show that the precision and recall of TPII are 91.72%and 94.51%respectively and a parallel implementation of TPII can achieve 213 million records digging and labelling within one hour,reaching near to support 1Gbps wirespeed inspection in practice.Our approach provides network service providers a practical way to collect PIIs for better services.