Name
Abstract | ||
|---|---|---|
Online medical primary diagnosis system, which can provide the pre-diagnosis service anywhere anytime, has attracted considerable interest. However, the flourish of online medical primary diagnosis system still faces many serious challenges since the sensitivity of personal health information and service provider's diagnosis model. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme based on k -nearest-neighbors classification (k-NN), called EPDK. With EPDK, medical users can ensure that their sensitive health information are not compromised during the online medical diagnosis process, and service provider can provide high-accuracy service without revealing its diagnosis model. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, a medical user preprocesses her/his query vector before sending out and the preprocessed vector is directly operated in the service provider without obtaining original data, meanwhile, the primary diagnosis result cannot be achieved by anyone except the medical user. Through extensive analysis, we show that EPDK can resist multifarious known security threats, and has significantly lower computation complexity than existing schemes. Moreover, performance evaluations via implementing EPDK in the real environment demonstrate that EPDK is highly efficient in terms of computation overhead. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 27TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN) | online medical primary diagnosis, privacy-preserving, k-nearest neighbors, polynomial aggregation |
Field | DocType | Citations |
The primary diagnosis,Computer science,Computer network,Encryption,Service provider,Information privacy,Medical diagnosis,Computation complexity,Personal health,Health information | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Zhu | 1 | 2 | 1.40 |
| Hui Zhu | 2 | 83 | 17.00 |
| Ximeng Liu | 3 | 135 | 31.84 |
| Hui Li | 4 | 202 | 34.25 |
| Fengwei Wang | 5 | 8 | 2.46 |
| Hao Li | 6 | 5 | 0.73 |