.png){kind=icon}
返回
Cloud-assisted secure biometric identification with sub-linear search efficiency
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-019-04401-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cloud computing has been one of the critical solutions to reduce heavy storage and computation burden of biometric identification. To protect the privacy of biometric data against untrusted cloud servers, outsourced biometric databases are usually encrypted by users. Performing biometric identification over encrypted data without revealing privacy to cloud servers attracts more and more attention. Several secure biometric identification solutions have been proposed to solve this challenging problem. However, these schemes still suffer from various limitations, such as low search efficiency and heavy computation burden on users. In this paper, we propose a novel cloud-assisted biometric identification scheme based on the asymmetric scalar-product preserving encryption (ASPE) and spatial data structures such as the R-tree index, which simultaneously achieves sub-linear search efficiency and low computation burden on users. Specifically, we construct an R-tree index on the biometric dataset and encrypt the index with ASPE. Then we modify the original search algorithm in the R-tree index and design a secure search algorithm based on ASPE to find the nearest neighbor result over the encrypted R-tree index. Through theoretical analysis and extensive experiments, we demonstrate the effectiveness and efficiency of our proposed approach.
Keyword:
Cloud computing
Privacy
Biometric identification
R-tree
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
.png){kind=icon}
分享收藏L-EncDB: A lightweight framework for privacy-preserving data queries in cloud computingL-encdb: 一个轻量级的云计算隐私保护数据查询框架
OAAI
OAAI分享收藏Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings基于物联网的高效传感器大数据采集-智能建筑中的处理和分析
PREAI
分享收藏
查看更多内容