A Privacy Preserving Upload Model for Crowd Sourced Health Care Monitoring System

In recent years governments become more concern about health care monitoring which cannot be accomplished without enhancing trust on underlying system as various citizens hesitate to upload their sample because of privacy reasons and obviously the governmental decisions are based on the data collected by various PHCs and third party medical agencies. The accuracy and authenticity of this third party owned data is always doubtful. Crowd sourcing(a collaborative framework) make its sound presence in development of large scale health projects Scientist also impressed from crowd sourcing which is a faster and better alternative to traditional methods for predicting and monitoring infectious diseases. However the success of this type of crowd sourcing depends on the trust on underlying system as the user is always looking firm commitment to preserve their privacy and win a promise of not being re-identified later. Here in this work we suggest a privacy protecting framework for upload process which could fulfill user's diverse privacy requirements while guaranteeing the quality of health care data.