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).
Algorithm 1: Privacy preserved community modeling.