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.

降维旨在减少大数据中的冗余信息，从而提高数据分析的效率。资源受限的企业或个人经常将这项耗时的工作外包给云端，以节省存储和计算资源。然而，由于监管不力，外包数据的隐私和安全一直是数据所有者的严重关切。在本文中，我们提出了一种基于增量非负矩阵分解（NMF）方法的隐私保护和可验证的数据降维外包方案。我们强调增量数据处理的重要性，利用NMF的特性，在考虑现实数据更新的情况下实现数据动态。此外，我们的方案还可以保持数据机密性并提供计算结果的可验证性。实验评估表明，该方案实现了高效率，为客户端节省了约80%以上的计算时间。