DivORAM: Towards a practical oblivious RAM with variable block size
Article
Liu, Z., Huang, Y., Li, J., Cheng, X. and Shen, C. 2018. DivORAM: Towards a practical oblivious RAM with variable block size. Information Sciences. 447, pp. 1-11. https://doi.org/10.1016/j.ins.2018.02.071
| Type | Article |
|---|---|
| Title | DivORAM: Towards a practical oblivious RAM with variable block size |
| Authors | Liu, Z., Huang, Y., Li, J., Cheng, X. and Shen, C. |
| Abstract | Oblivious RAM (ORAM) is important for applications that require hiding access patterns. Many ORAM schemes have been proposed but most of them support only storing blocks of the same size. For the variable length data blocks, they usually fill them upto the same length before uploading, which leads to an increase in storage space and network bandwidth usage. To develop the first practical ORAM with variable block size, we proposed the “DivORAM” by remodeling the tree-based ORAM structure. It employs an additively homomorphic encryption scheme (Damgård–Jurik cryptosystem) executing at the server side to save the client computing overhead and the network bandwidth cost. As a result, it saves network bandwidth 30% comparing with Ring ORAM and 40% comparing with HIRB ORAM. Experiment results show that the response time of DivORAM is 10 × improved over Ring ORAM for practical parameters. |
| Keywords | Oblivious RAM; Data privacy; Cloud computing; Access pattern |
| Research Group | Artificial Intelligence group |
| Publisher | Elsevier |
| Journal | Information Sciences |
| ISSN | 0020-0255 |
| Electronic | 1872-6291 |
| Publication dates | |
| Online | 06 Mar 2018 |
| 01 Jun 2018 | |
| Publication process dates | |
| Deposited | 09 Jul 2018 |
| Accepted | 26 Feb 2018 |
| Output status | Published |
| Accepted author manuscript | License |
| Copyright Statement | © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2018.02.071 |
| Web of Science identifier | WOS:000430902400001 |
| Language | English |
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Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873