An Exploration to Location Based Service and Its Privacy Preserving Techniques: A Survey

Gupta, Ruchika; Rao, Udai Pratap

doi:10.1007/s11277-017-4284-2

Cited by 56 publications

(36 citation statements)

References 47 publications

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“…b) The problem statement Location privacy is one of the key issues that needs to be solved. There are various schemas proposed by different researchers [8,9,[18][19][20][21][22][23]. Out of that Collaborative TTP Free model [24][25][26] is one of the best technique to provide highest location privacy.…”

Section: ) Random Chainingmentioning

confidence: 99%

Decentralized collaborative TTP free approach for privacy preservation in location based services

Rathod¹,

Shah²,

Jariwala³

2019

IJECE

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In recent trends, growth of location based services have been increased due to the large usage of cell phones, personal digital assistant and other devices like location based navigation, emergency services, location based social networking, location based advertisement, etc. Users are provided with important information based on location to the service provider that results the compromise with their personal information like user’s identity, location privacy etc. To achieve location privacy of the user, cryptographic technique is one of the best technique which gives assurance. Location based services are classified as Trusted Third Party (TTP) & without Trusted Third Party that uses cryptographic approaches. TTP free is one of the prominent approach in which it uses peer-to-peer model. In this approach, important users mutually connect with each other to form a network to work without the use of any person/server. There are many existing approaches in literature for privacy preserving location based services, but their solutions are at high cost or not supporting scalability. In this paper, our aim is to propose an approach along with algorithms that will help the location based services (LBS) users to provide location privacy with minimum cost and improve scalability.

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Section: ) Random Chainingmentioning

confidence: 99%

Decentralized collaborative TTP free approach for privacy preservation in location based services

Rathod¹,

Shah²,

Jariwala³

2019

IJECE

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“…Several directions of investigation were taken by researchers to mitigate location privacy threats. Various Location Privacy Protection Mechanisms have been proposed [17], [7], [30], [5]. Some of them rely on formal privacy guarantees such as k-anonymity [31] or differential privacy [12], some of them target sensitive information and try to hide them (e.g., POIs.…”

Section: Related Workmentioning

confidence: 99%

“…To tackle these threats, many Location Privacy Protection Mechanisms (LPPMs) were proposed in the literature. LPPMs protect user location information by relying on a wide variety of techniques such as perturbation, generalization and fake data generation [17].…”

Section: Introductionmentioning

confidence: 99%

MooD

Khalfoun

Maouche

Mokhtar

et al. 2019

Proceedings of the 20th International Middleware Conference

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With the increasing development of handheld devices, Location Based Services (LBSs) became very popular in facilitating users' daily life with a broad range of applications (e.g. traffic monitoring, geo-located search, geo-gaming). However, several studies have shown that the collected mobility data may reveal sensitive information about end-users such as their home and workplaces, their gender, political, religious or sexual preferences. To overcome these threats, many Location Privacy Protection Mechanisms (LPPMs) were proposed in the literature. While the existing LPPMs try to protect most of the users in mobility datasets, there is usually a subset of users who are not protected by any of the existing LPPMs. By analogy to medical research, there are orphan diseases, for which the medical community is still looking for a remedy. In this paper, we present MooD, a fine-grained multi-LPPM user-centric solution whose main objective is to find a treatment to mobile users' orphan disease by protecting them from re-identification attacks. Our experiments are conducted on four real world datasets. The results show that MooD outperforms its competitors, and the amount of user mobility data it is able to protect is in the range between 97.5% to 100% on the various datasets. CCS Concepts • Security and privacy → Pseudonymity, anonymity and untraceability.

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“…Therefore, it becomes difficult for the third party to identify the actual user [21]. Research shows that constructing the cloak of user location does not ensure the absolute user privacy [22], however, [23] [24] propose another alternatives using TTP based and TTP free architectures.…”

Section: Literature Reviewmentioning

confidence: 99%