Long-term location privacy protection for location-based services in mobile cloud computing

Tang, Feilong; Li, Jie; You, Ilsun; Guo, Mei

doi:10.1007/s00500-015-1703-8

Cited by 22 publications

(11 citation statements)

References 33 publications

(35 reference statements)

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“…Time consumption [21,55,22,24,63,26,27,29,31,66,33,34,67,68,37,7,38,39,40,41,42,57,71,58,59,75,44,47,48,49,51,88] Communication overhead [24,64,65,32,66,33,67,68,36,7,91,85,86,41,56,42,57,71,58,75,49,…”

Section: Resources Usage Metricsmentioning

confidence: 99%

Privacy and data protection in mobile cloud computing: A systematic mapping study

2020

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As a result of a shift in the world of technology, the combination of ubiquitous mobile networks and cloud computing produced the mobile cloud computing (MCC) domain. As a consequence of a major concern of cloud users, privacy and data protection are getting substantial attention in the field. Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, however, no specific investigation highlights the results of the existing studies in privacy and data protection. In addition, there are no particular exploration highlights trends and open issues in the domain. Accordingly, the objective of this paper is to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues. In this investigation, a systematic mapping study was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a collection of 74 primary studies were selected. As a result, the present data privacy threats, attacks, and solutions were identified. Also, the ongoing trends of data privacy exercise were observed. Moreover, the most utilized measures, research type, and contribution type facets were emphasized. Additionally, the current open research issues in privacy and data protection in MCC were highlighted. Furthermore, the results demonstrate the current state-of-the-art of privacy and data protection in MCC, and the conclusion will help to identify research trends and open issues in MCC for researchers and offer useful information in MCC for practitioners.

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Section: Resources Usage Metricsmentioning

confidence: 99%

Privacy and data protection in mobile cloud computing: A systematic mapping study

2020

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“…In order to provide high-quality services, the cloud manager first customizes virtual machines (VMs) for vehicles to support LBS according to the vehicles' demands. 7 Moreover, a major concern that hinders IoV application is cost. The Lee and Lai 8 and Bayat et al 9 are currently a better solution to improve security and improve computational efficiency, but these schemes use bilinear pairing operations.…”

Section: Introductionmentioning

confidence: 99%

QBPP: Quality of services–based location privacy protection method for location-based services in cloud-enabled Internet of vehicles

Chen

Wang

2019

International Journal of Distributed Sensor Networks

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Location-based services has been widely applied in cloud-enabled Internet of vehicles. Within these services, location privacy issues have captured significant attention. Vehicles use the technology of anonymity to implement occultation, the location is not revealed. In this process, large-scale data transmissions can reduce the quality of services. In order to ensure location privacy and high-quality services, the cloud manager customizes virtual machines for vehicles to support location-based services according to the vehicles’ demands. To achieve better performance, this article presents a conditional anonymity method that does not use bilinear pairings to address the problem of privacy disclosure by using discrete logarithm problem and Diffie–Hellman problem. Moreover, asymmetric key algorithms are used in the Internet of vehicles environment to reduce the cost. To guarantee secure data transmission in Internet of vehicles, the batch validation technique is used to address data integrity. Our theoretical security analysis and experiments show that the proposed scheme is secure in compared attack models, such as impersonation attacks, replay attacks, the man-in-the-middle attacks, and so on. Our proposed scheme ensures the security requirements such as message authentication, location privacy protection, and traceability, while lowering transmission and computation cost.

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“…A large number of techniques [10][11][12][13][14][15][16][17][18][19][20][21][22] have been proposed to address the privacy preservation issue in location-based services. Some of them are based on the cloaking technique, which employs the k-anonymity model to protect user's location privacy.…”

Section: Introductionmentioning

confidence: 99%

Efficient location privacy algorithm for Internet of Things (IoT) services and applications

Sun

Chang

Ramachandran

et al. 2017

Journal of Network and Computer Applications

141

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Location-based Services (LBS) have become a very important area for research with the rapid development of Internet of Things (IoT) technology and the ubiquitous use of smartphones and social networks in our daily lives. Although users can enjoy a lot of flexibility and conveniences from the LBS with IoT, they may also lose their privacy. Untrusted or malicious LBS servers with all users' information can track users in various ways or release personal data to third parties. In this work, we first analyze the current dummy-location selection (DLS) algorithm-an efficient location privacy preservation approach and design an attack algorithm for DLS (ADLS) for test emerging IoT security. For efficiently preserving user's location privacy, we propose a novel dummy location privacy-preserving (DLP) algorithm by considering both computational costs and various privacy requirements of different users. Extensive simulation experiments have been carried out to evaluate the efficiency of the proposed schemes. Evaluation results show that the ADLS algorithm has a high probability of identifying the user's real location out from chosen dummy locations in the DLS algorithm. Our proposed DLP algorithm has clear advantages over the DLS algorithm in term of lower probability of revealing the user's real location and improved computational cost and efficiency (i.e., time, speed, accuracy, and complexity) while preserve the same privacy level as DLS algorithm.

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Long-term location privacy protection for location-based services in mobile cloud computing

Cited by 22 publications

References 33 publications

Privacy and data protection in mobile cloud computing: A systematic mapping study

Privacy and data protection in mobile cloud computing: A systematic mapping study

QBPP: Quality of services–based location privacy protection method for location-based services in cloud-enabled Internet of vehicles

Efficient location privacy algorithm for Internet of Things (IoT) services and applications

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