Security in Emerging 4G Networks

Abdo, Jacques Bou; Chaouchi, Hakima; Demerjian, Jacques

doi:10.1007/978-1-4471-5164-7_12

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…(i) Location privacy: in crowdsourced LBS, locationidentity relationship is relatively easy to be exposed using correlation attacks [14].…”

Section: Privacy Requirements Of Crowdsourced Lbs Applicationsmentioning

confidence: 99%

“…In this subsection, we survey the LPPMs that were proposed for legacy LBS [14] which are as follows: (ii) Private Information Retrieval (PIR): both request and reply are encrypted, leaving the server unable to identify the sender or the request. This mechanism is suitable for legacy LBS but impractical in crowdsourcing, since the search engines need to send the crowd a plaintext job.…”

Section: Location Privacy Preserving Mechanismsmentioning

confidence: 99%

“…Matching user's location to his queries has severe security consequences, since location can be related to identity [14] and queries can be related to user preferences, interests, ideas, and beliefs. Being able to match user's identity to preference can be used for customer profiling and behavior expectancy.…”

Section: Introductionmentioning

confidence: 99%

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Extended Privacy in Crowdsourced Location-Based Services Using Mobile Cloud Computing

Abdo

Bourgeau

Demerjian

et al. 2016

Mobile Information Systems

Self Cite

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Crowdsourcing mobile applications are of increasing importance due to their suitability in providing personalized and better matching replies. The competitive edge of crowdsourcing is twofold; the requestors can achieve better and/or cheaper responses while the crowd contributors can achieve extra money by utilizing their free time or resources. Crowdsourcing location-based services inherit the querying mechanism from their legacy predecessors and this is where the threat lies. In this paper, we are going to show that none of the advanced privacy notions found in the literature except forK-anonymity is suitable for crowdsourced location-based services. In addition, we are going to prove mathematically, using an attack we developed, thatK-anonymity does not satisfy the privacy level needed by such services. To respond to this emerging threat, we will propose a new concept, totally different from existing resource consuming privacy notions, to handle user privacy using Mobile Cloud Computing.

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“…(i) Location privacy: in crowdsourced LBS, locationidentity relationship is relatively easy to be exposed using correlation attacks [14].…”

Section: Privacy Requirements Of Crowdsourced Lbs Applicationsmentioning

confidence: 99%

Section: Location Privacy Preserving Mechanismsmentioning

confidence: 99%

See 1 more Smart Citation

Extended Privacy in Crowdsourced Location-Based Services Using Mobile Cloud Computing

Abdo

Bourgeau

Demerjian

et al. 2016

Mobile Information Systems

Self Cite

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Privacy using mobile cloud computing

Abdo¹,

Demerjian²,

Chaouchi³

et al. 2015

2015 Fifth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP)

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Crowdsourcing mobile applications are of increasing importance due to their suitability in providing personalized and better matching replies. Crowdsourcing location based services inherit the querying mechanism from its legacy predecessors and this is where the threat lays. In this paper we are going to prove mathematically that the mechanism with highest privacy level "Footprint" does not satisfy the privacy level needed by crowdsourced location based services and then prove that higher privacy level and lower overhead is achieved when these services are implemented in Operator centric mobile cloud architecture.

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Security in Emerging 4G Networks

Cited by 2 publications

References 13 publications

Extended Privacy in Crowdsourced Location-Based Services Using Mobile Cloud Computing

Extended Privacy in Crowdsourced Location-Based Services Using Mobile Cloud Computing

Privacy using mobile cloud computing

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