2017•DEStech Transactions on Computer Science and EngineeringRequires access

Privacy-area Aware All-dummy-based Location Privacy Algorithms for Location-based Services

Huan Qi Zhao, Xiao-Ling YI, Jiao-Long Wan

Open publisher page 8 citations

Abstract

Location-Based Services (LBSs) have become one of the most popular activities in our daily life. With the rapid advance of LBSs, there are more threats to users’ privacy. For this reason, while enjoying the convenience provided by LBSs, we have to protect our location privacy. In this paper, we propose two all-dummy-based location privacy algorithms to achieve k-anonymity for privacy-area aware users in LBSs. Different from previous work, on the client side, our method can prevent the center attack and border attack through transforming the actual location to an anchor and the request we send to the LBSs doesn’t contain the actual location of the user. On the server side, we use a rough query before precise query to reduce the processing time and transmission bandwidth. It only returns the results that the client needs. Evaluation results show that our methods can provide more effective privacy protection and lower computation and communication cost.

About this research paper

What this paper is about

Location-Based Services (LBSs) have become one of the most popular activities in our daily life. With the rapid advance of LBSs, there are more threats to users’ privacy. For this reason, while enjoying the convenience provided by LBSs, we have to protect our location privacy. In this paper, we propose two all-dummy-based location privacy algorithms to achieve k-anonymity for privacy-area aware users in LBSs. Different from previous work, on the client side, our method can prevent the center attack and border attack through transforming the actual location to an anchor and the request we send to the LBSs doesn’t contain the actual location of the user. On the server side, we use a rough query before precise query to reduce the processing time and transmission bandwidth. It only returns the results that the client needs. Evaluation results show that our methods can provide more effective privacy protection and lower computation and communication cost.

Why it matters

OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Location-Based Services (LBSs) have become one of the most popular activities in our daily life. With the rapid advance of LBSs, there are more threats to users’ privacy. For this reason, while enjoying the convenience provided by LBSs, we have to protect our location privacy. In this paper, we propose two all-dummy-based location privacy algorithms to achieve k-anonymity for privacy-area aware users in LBSs. Different from previous work, on the client side, our method can prevent the center attack and border attack through transforming the actual location to an anchor and the request we send to the LBSs doesn’t contain the actual location of the user. On the server side, we use a rough query before precise query to reduce the processing time and transmission bandwidth. It only returns the results that the client needs. Evaluation results show that our methods can provide more effective privacy protection and lower computation and communication cost.

Key concepts: Computer science, k-anonymity, Location-based service, Anonymity, Location data, Privacy protection, Client-side, Computer security

Related papers

Back to paper searchBrowse research topicsOriginal source
Privacy-area Aware All-dummy-based Location Privacy Algorithms for Location-based Services — Research Paper | ScholarLens