  Matrix completion has important applications in trajectory recovery and
mobile social networks. However, sending raw data containing personal,
sensitive information to cloud computing nodes may lead to privacy exposure
issue.The privacy-preserving matrix completion is a useful approach to perform
matrix completion while preserving privacy. In this paper, we propose a
high-performance method for privacy-preserving matrix completion. First,we use
a lightweight encryption scheme to encrypt the raw data and then perform matrix
completion using alternating direction method of multipliers (ADMM). Then,the
complemented matrix is decrypted and compared with the original matrix to
calculate the error. This method has faster speed with higher accuracy. The
results of numerical experiments reveal that the proposed method is faster than
other algorithms.
