Velocity similarity anonymization for continuous query Location based services

Gustav, Yankson H.; Wang, Yong; Domenic, M. Kamenyi; Zhang, Fengli; Memon, Imran

doi:10.1109/iccps.2013.6893578

Cited by 30 publications

(23 citation statements)

References 7 publications

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“…In previous work [7,11,18,19,24], we proposed the velocity dynamic cloaking algorithm and argued that even if clients with the same transportation modes are cloaked together, they may have different moving trend and status so it was important to consider their moving trend during cloaking. We therefore introduced cloaking clients with similar velocity and acceleration to preserve privacy in continuous query LBSs.…”

Section: Related Workmentioning

confidence: 99%

“…This section will be divided into three parts, the evaluation criteria and metrics are discussed in Section A. In section B, we describe the experimental setup and evaluate results comparing it with that previous work V-DCA [18], DSDCA [19], AVD-DCA [7], D-TC [20] and GCA [11],in which we considered similar velocity and acceleration for cloaking in the algorithm.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…These steps continue until there are no clients to be cloaked together anymore and velocity and changing distances will be calculated (steps [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The cloaked region requirement of k local and δp is treated as the qualified cloaked region (steps [19][20][21].The subsequent snapshot i in the query lifetime, we respectively check the satisfaction of the δq by adding each client in R i−1 , R i−2 , . .…”

Section: Algorithm Depictionmentioning

confidence: 99%

“…Consider two mobile clients i and j with location l(x i y i ) and l(x j y j ) with two angles of direction i and i respectively to an origin location l(x o, y o ). The directional similarity sim of client i and j can be calculated as follows [19]:…”

Section: Preliminariesmentioning

confidence: 99%

“…We carry out experiments and analyze our algorithm based on a real world map to test its efficiency and performance. Compare with existing privacy protection methods such as V-DCA [18], DSDCA [19], AVD-DCA [7], D-TC [20] and GCA [11], we run a set of simulation experiments to evaluate our proposal and make a comparison of the performance.…”

Section: Introductionmentioning

confidence: 99%

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Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Memon

2015

Wireless Pers Commun

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Location based services (LBSs) are gaining importance due to the advancement in mobile networks and positioning technologies. The proliferation of location-based services in recent years has highlighted the need to consider location privacy. This has led to the development of methods enhancing location privacy, and to the investigation of reasons for sharing location information. While computational attacks on location privacy and their prevention have attracted a lot of research, attacks based on humans strategies and tactics have mostly been considered implicitly. Nevertheless, in querying LBSs, a user send its exact locations to their location service providers but in the process, location information of the users are misused either purposefully or otherwise by service providers creating privacy issues for users. It has therefore become important that mechanisms necessary to protect the privacy of users are adopted when querying location based services. It's on this premise that we introduced a novel query privacy algorithm called the authentication speed dynamic transportation mode cloaking algorithm for continuous query LBSs that considers users' similarity in speed, direction and travelling with the same transport mode for cloaking for anonymization. Experimental evaluation of the algorithm on a real world map shows that our model ensures total privacy for users, enhanced privacy guarantee, improves quality of service significantly and achieved an excellent performance measure also we compare our method with existing privacy protection methods such as V-DCA, DSDCA, AVD-DCA,D-TC and GCA.

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Section: Related Workmentioning

confidence: 99%

Section: Experiments and Evaluationmentioning

confidence: 99%

Section: Algorithm Depictionmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Memon

2015

Wireless Pers Commun

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A new ‐map mechanism for mobility traces privacy

Lingyuan

Zhang

et al. 2020

Concurrency and Computation

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A major concern of the deployment of location based services (LBSs) is the safeguards of the user's location data collected by service providers, since personal location data may imply sensitive private information. However, most available works proposed so far rely on syntactic privacy models such as k-anonymity and location perturbation, which are proved to quiver in the balance with privacy and usability requirements. In this article, we provide a new-map mechanism to help users better understand the privacy/accuracy tradeoff process and preserve location data. In our-map model, the user can specify a geographic region to hide her precise location and suppress the following queries in the same area to meet her privacy requirement, while maintaining and understanding its usability. In addition, we propose a new notion of Δt-privacy based on differential privacy to account for the temporal-spatial correlation and history correlation in the case of crossing region, which is the major privacy concern of a moving user's trace. Finally, we evaluate our framework by using an online LBS with real-world data sets. The results not only indicate that the-map is significantly useful for identifying the privacy and utility tradeoffs but also show the effectiveness and practicality of the proposed Δt-privacy.

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Privacy‐preserving enhanced dummy‐generation technique for location‐based services

Parmar

Rao

2022

Concurrency and Computation

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Location-based services (LBS) has become an intrinsic part of our everyday life. However, the flexibility and convenience provided by LBS are at the cost of user privacy since untrusted LBS server can leak private information of users. To overcome the privacy issues observed in LBS, a novel dummy-generation based privacy preservation technique is proposed in this article. The proposed dummy-generation technique is a circle-based technique which generates dummy locations in the circle area and is effective against center-of-anonymized spatial region attack, map-matching attack, and location-homogeneity attack. Additionally, an edge computing enabled framework is proposed, which can be used in the IoT environment. The edge computing enabled framework helps in handling the resource poverty issues of the service requesting devices and provides the low latency solution. The security analysis of our proposed dummy-generation technique reflects that the proposed technique is resilient to specific attacks for different adversary attack models. The results obtained through simulations suggest that our technique performs better than the pre-existing techniques.

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Velocity similarity anonymization for continuous query Location based services

Cited by 30 publications

References 7 publications

Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

A new ‐map mechanism for mobility traces privacy

Privacy‐preserving enhanced dummy‐generation technique for location‐based services

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