Research Article
Privacy Preserved Self-Awareness on the Community via Crowd Sensing
Algorithm 1
Privacy preserved community modeling.
Input: A user’s data set with observational data | , user’s data number probability | coefficient . | Output: The user’s polynomial approximation function | model , and the coefficient matrix . | (1) Each user transforms his data into the form , | where is the probability of in the user’s data set; | (2) All users build their own data models with the | polynomial approximation function algorithm in a | distributed system; | (3) Each user slices his data distribution model by Eq. (3) to | obtain the coefficient matrix ; | (4) For each user, one of the coefficient matrix rows is kept | by himself and the remaining row pieces are sent to | other users randomly; | (5) Each user collects all the received matrix rows, mixes | them and send the mixed result to the data analyst, in | the same way, the data analyst can reconstruct the final | model in the community by Eq. (5). |
|