A user location anonymization method for location based services in a real environment

Suzuki, Ayako; Iwata, Makoto; Arase, Yasuji; Hara, Takahiro; Xie, Xing; Nishio, Shojiro

doi:10.1145/1869790.1869846

Cited by 50 publications

(41 citation statements)

References 3 publications

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“…In papers [10]- [12], authors focused on the need on the real environment considering physical constraints, and proposed the novel dummy generation algorithms called DumGrid and Dum-P [10], [11]. Dum-Grid generates dummies around the user in a grid pattern, while Dum-P assures a more realistic mobility model to generate dummies along with the user movements.…”

Section: Related Workmentioning

confidence: 99%

A Novel Dummy-Based Mechanism to Protect Privacy on Trajectories

Sun

2014

2014 IEEE International Conference on Data Mining Workshop

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In recent years, wireless communication technologies and accurate positioning devices enable us to enjoy various types of location-based services. However, revealing users location information to potentially untrusted LBS providers is one of the most significant privacy threats in location-based services. The dummy-based privacy-preserving approach is a popular technology that can protect real trajectories from exposing to attackers. Moreover, it does not need a trusted third part, while guaranteeing the quality of service. When user requests a service, dummy trajectories anonymize the real trajectory to satisfy privacy-preserving requirements.In this paper, we propose a new privacy model that includes three reasonable privacy metrics. We also design a new algorithm named adaptive dummy trajectories generation algorithm (ADTGA) to derive uniformly distributed dummy trajectories. Dummy trajectories generated by our algorithm can achieve stricter privacy-preserving requirements based on our privacy model. The experimental results show that our proposed algorithm can use fewer dummy trajectories to satisfy the same privacy-preserving requirement than existing algorithms, and the distribution of dummy trajectories is more uniformly.

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Section: Related Workmentioning

confidence: 99%

A Novel Dummy-Based Mechanism to Protect Privacy on Trajectories

Sun

2014

2014 IEEE International Conference on Data Mining Workshop

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“…In dummy based technique [24][25][26], it generates false user locations called dummies and combine them with the actual user location into the request. The user can refer to the list corresponding to his/her actual location, i.e., filter out the other lists.…”

Section: B Dummy-based Techniquementioning

confidence: 99%

“…You et al [25] improve this technique by generating consistent movement patters for dummies in a long run. Suzuki et al [26] enhance it by generating scattered dummies around the user based on the user's current location and previous dummies locations. They also consider dummies locations according to the real road information.…”

Section: B Dummy-based Techniquementioning

confidence: 99%

K-Anonymous Cloaking Algorithm Based on Weighted Adjacency Graph for Preserving Location Privacy

Hossain

Jang

et al. 2012

2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications

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The propagation of position identifying devices, such as GPS (Global Positioning System), becomes increasingly a privacy threat in location-based services (LBSs). However, in order to enjoy such services, the user must precisely disclose his/her exact location to the LBS. So, it is a key challenge to efficiently preserve user's privacy while accessing LBS. For this, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, it suffers from privacy attack. Therefore, we aim to provide the solutions which can preserve user privacy by utilizing k-anonymity mechanism. In this paper, we propose a weighted adjacency graph based kanonymous cloaking technique that can provide protection to user and also reduce bandwidth usages. Our cloaking approach efficiently supports k-nearest neighbor queries without revealing private information of the query initiator. We demonstrate via experimental results that our algorithm yields much better performance than the existing one.

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“…User location anonymization methods using dummies are the representative approach [2,9,12,14]. Through generating dummies and sending their locations with the user's actual location to the server [2,[11][12][13], the user can hide the true location. According to the query, the server is performed in accordance with all position candidates and the server returns the query results to the user.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao¹,

Yi²,

Wan³

2017

dtcse

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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.

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A user location anonymization method for location based services in a real environment

Cited by 50 publications

References 3 publications

A Novel Dummy-Based Mechanism to Protect Privacy on Trajectories

A Novel Dummy-Based Mechanism to Protect Privacy on Trajectories

K-Anonymous Cloaking Algorithm Based on Weighted Adjacency Graph for Preserving Location Privacy

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

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