Name
Abstract | ||
|---|---|---|
Hosting data query services in public clouds is an attractive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confidential range/kNN query services built on top of the random space perturbation (RASP) method. The RASP approach provides a privacy guarantee practical to the setting of cloud-based computing, while enabling much faster query processing compared to the encryption-based approach. This demonstration will allow users to more intuitively understand the technical merits of the RASP approach via interactive exploration of the visual interface. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.14778/2733004.2733061 | PVLDB |
Field | DocType | Volume |
Data mining,Visual interface,Confidentiality,Rasp,Computer science,Encryption,Information privacy,Database,Data query,Cloud computing,Scalability | Journal | 7 |
Issue | ISSN | Citations |
13 | 2150-8097 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zohreh Alavi | 1 | 6 | 1.87 |
| Lu Zhou | 2 | 0 | 1.01 |
| James Powers | 3 | 35 | 2.95 |
| Keke Chen | 4 | 761 | 49.04 |