Achieving big data privacy via hybrid cloud
Xueli Huang, Xiaojiang Du · 2014
Nowadays the amount of data is being produced exponentially with the rapid development of electronic technology and communication, which makes it hard to cost-effectively store and manage these big data. Cloud computing, a new business model, is considered as one of most attractive solutions for big data, and provides the advantage of reduced cost through sharing of computing and storage resources. However, the growing concerns in term of the privacy of data stored in public cloud have slowed down the adoption of cloud computing for big data because sensitive information may be contained among the big data or the data owner themselves do not want any other people to scan their data. Since the data volume is huge and mobile devices are widely used, the traditional cryptographic approach are not suitable for big data. In this paper, we propose an efficient scheme for image data, which has much more volume than text data. We evaluate our scheme in real networks (including Amazon EC2), and our experimental results on image show that: (1) our scheme achieves privacy but only use 1/585.8~1/398.6 the time of the AES algorithm; (2) the delay of our hybrid-cloud-based scheme is only 3%~5% more than that of the traditional public-cloud-only approach.