A Game-Theoretic Framework to Preserve Location Information Privacy in Location-Based Service Applications

Tefera, Mulugeta Kassaw; Yang, Xiaolong

doi:10.3390/s19071581

Cited by 8 publications

(8 citation statements)

References 46 publications

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“…But the presented method is specifically designed for Vehicular Social Networks. Tefera and Yang 18 considered privacy analysis based on individuals' identity in a location‐based service using the prisoner's dilemma, but their view was to analyze this issue statically.…”

Section: Related Workmentioning

confidence: 99%

A privacy‐preserving mechanism for social mobile crowdsensing using game theory

Esmaeilyfard

Esmaili

2022

Trans Emerging Tel Tech

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Smartphones and mobile networks have created a new paradigm called mobile crowdsensing for data gathering and processing about a large-scale phenomenon. This paradigm's development allows untrustworthy people to gather large amounts of information over time from the reported events. In this regard, mobile crowdsensing increases the possibility of violating users' privacy. Recently, social communication has added potential capabilities to mobile crowdsourcing environments. Based on these social communications, this article presents an analysis of user's interactions for privacy-preserving using a signaling game with a dynamic approach and incomplete information. First, the analysis identifies the pure-strategy profiles and mixed-strategy profiles and the user's best strategy for accepting access requests in a single time slot. This analysis is then extended into a dynamic multistage game. In this analysis, users' beliefs about the violation of their privacy are analyzed and updated at every stage. As a result, the best response from users will be determined. Following this analysis, a mechanism is presented for privacy-preserving in social mobile crowdsensing. The experimental results have also indicated the success of the proposed method in dealing with different types of user interaction.

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Section: Related Workmentioning

confidence: 99%

A privacy‐preserving mechanism for social mobile crowdsensing using game theory

Esmaeilyfard

Esmaili

2022

Trans Emerging Tel Tech

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“…where R u i (T 1 + (n − 1)T) represents the user reputation score of u i at T 1 + (n − 1)T, and the specific calculation is as in formula (10). According to formula (7),…”

Section: Beta Reputation Mechanismmentioning

confidence: 99%

“…We call the above process that obtains a user's profile connecting user identities attack (CUIA). At present, most researches focus on the protection of user privacy in terms of query content [2,3,[10][11][12] and data publication [8,13,14]. However, for data publication, few studies are investigating the privacy protection of review publication in CSLBSSs.…”

Section: Introductionmentioning

confidence: 99%

Cross-Platform Strong Privacy Protection Mechanism for Review Publication

Wang

Xin

et al. 2021

Security and Communication Networks

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As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

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“…Privacy is a guarantee that information that is gathered might be only observed or deciphered by the authorized parties. The monitoring application can be misused by the attacker to hunt the animals, hence severely affecting the aim of the monitoring application Location privacy is not just limited to remote WSN, but also information leakage in the location-based services by third party observers [12] and node location privacy in the industrial wireless sensor network [13] is also a big threat to privacy issues.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

Jan

Al-Bayatti

Alalwan

et al. 2019

Sensors

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Wireless Sensor Network is a network of large number of nodes with limited power and computational capabilities. It has the potential of event monitoring in unattended locations where there is a chance of unauthorized access. The work that is presented here identifies and addresses the problem of eavesdropping in the exposed environment of the sensor network, which makes it easy for the adversary to trace the packets to find the originator source node, hence compromising the contextual privacy. Our scheme provides an enhanced three-level security system for source location privacy. The base station is at the center of square grid of four quadrants and it is surrounded by a ring of flooding nodes, which act as a first step in confusing the adversary. The fake node is deployed in the opposite quadrant of actual source and start reporting base station. The selection of phantom node using our algorithm in another quadrant provides the third level of confusion. The results show that Dissemination in Wireless Sensor Networks (DeLP) has reduced the energy utilization by 50% percent, increased the safety period by 26%, while providing a six times more packet delivery ratio along with a further 15% decrease in the packet delivery delay as compared to the tree-based scheme. It also provides 334% more safety period than the phantom routing, while it lags behind in other parameters due to the simplicity of phantom scheme. This work illustrates the privacy protection of the source node and the designed procedure may be useful in designing more robust algorithms for location privacy.

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A Game-Theoretic Framework to Preserve Location Information Privacy in Location-Based Service Applications

Cited by 8 publications

References 46 publications

A privacy‐preserving mechanism for social mobile crowdsensing using game theory

A privacy‐preserving mechanism for social mobile crowdsensing using game theory

Cross-Platform Strong Privacy Protection Mechanism for Review Publication

An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

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