.png){kind=icon}
返回
Anonymous and Linkable Location-Based Services
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2022.3180412.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the location-based services (LBS) scheme, the vehicle user sends his query to the LBS server and gets the data corresponding to the requested query from it. Therefore, vehicle user's query privacy and LBS server's data privacy are serious issues in LBS. Several LBS schemes, like k-anonymity, private-information retrieval, and homomorphic encryption-based, have been proposed to solve these issues. These schemes either provide vehicle user's query privacy or LBS server's data privacy. Some of the oblivious transfer (OT) protocol-based LBS schemes preserve vehicle users' query privacy and LBS server's data privacy. However, the OT-based LBS schemes do not preserve vehicle user's identity privacy. Moreover, the efficiency (communication and computation costs) of the OT protocol-based LBS schemes depends on the LBS server's database size, which is very high. In this work, we propose an anonymous linkable location-based services (AL2BS) scheme using linkable ring signcryption technique. The scheme provides vehicle user's query privacy and LBS server's data privacy without revealing the vehicle user's identity. The efficiency of the AL2BS scheme is independent of the size of the LBS server's database. The AL2BS scheme requires only one pairing operation on both ends and a few point multiplications. The theoretical and experimental results show that the performance of the AL2BS scheme in terms of communication and computation costs is better than the existing LBS schemes.
Keyword:
Location-based services
query privacy
anonymity
ring signature
bilinear pairing
signcryption
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
.png){kind=icon}
Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers
OAAI
OAAI分享收藏Highly Efficient Privacy Preserving Location-Based Services with Enhanced One-Round Blind Filter
PREAI
分享收藏分享收藏
查看更多内容