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| Content Provider | PubMed Central |
|---|---|
| Author | Tian, Feng Gui, Xiaolin An, Jian Yang, Pan Zhao, Jianqiang Zhang, Xuejun |
| Copyright Year | 2014 |
| Abstract | As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC∗) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC∗ and DSC are more secure than SHC, and DSC achieves the best index generation performance. |
| Related Links | http://dx.doi.org/10.1155/2014/108072 |
| Starting Page | 108072 |
| File Format | |
| ISSN | 23566140 |
| e-ISSN | 1537744X |
| Journal | The Scientific World Journal |
| Volume Number | 2014 |
| Language | English |
| Publisher | Hindawi Publishing Corporation |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Rights Holder | Hindawi Publishing Corporation |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Environmental Science(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine Biochemistry, Genetics and Molecular Biology Environmental Science |
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