Secure and verifiable outsourced data dimension reduction on dynamic data

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. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The dimensionality reduction aims at reducing redundant information in big data for efficient data analysis. Enterprises or individuals with limited resources often outsource this task to the cloud. However, privacy and security concerns arise due to inadequate supervision. A proposed scheme based on incremental NMF method addresses these concerns while ensuring data confidentiality and verifiability of computation results. Experiment evaluation shows the scheme's high efficiency in saving over 80% computation time for clients.