Secure and verifiable outsourced data dimension reduction on dynamic data

Abstract Dimensionality reduction aims at reducing redundant information in big data and hence making data analysis more efficient. Resource-constrained enterprises or individuals often outsource this time-consuming job to the cloud for saving storage and computing resources. However, due to inadequate supervision, the privacy and security of outsourced data have been a serious concern to data owners. In this paper, we propose a privacy-preserving and verifiable outsourcing scheme for data dimension reduction, based on incremental Non-negative Matrix Factorization (NMF) method. We emphasize the importance of incremental data processing, exploiting the properties of NMF to enable data dynamics in consideration of data updating in reality. Besides, our scheme can also maintain data confidentiality and provide verifiability of the computation result. Experiment evaluation has shown that the proposed scheme achieves high efficiency, saving about more than 80% computation time for clients.