Location Privacy in Cognitive Radios With Multi-Server Private Information Retrieval

Grissa, Mohamed; Yavuz, Attila A.; Hamdaoui, Bechir

doi:10.1109/tccn.2019.2922300

Cited by 16 publications

(5 citation statements)

References 43 publications

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“…Information Retrieval (PIR) [25] was used to achieve full privacy protection. The key idea of the PIR technique depends on mathematical principle.…”

Section: Related Workmentioning

confidence: 99%

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Alyousef¹,

Srinivasan²,

Alrahhal³

et al. 2022

IJACSA

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Location-based services (LBSs) have received a significant amount of recent attention from the research community due to their valuable benefits in various aspects of society. In addition, the dependency on LBS in the performance of daily tasks has increased dramatically, especially after the spread of the COVID-19 pandemic. LBS users use their real location to build LBS queries to take benefits. This makes location privacy vulnerable to attacks. The privacy issue is accentuated if the attacker is an LBS provider since all information about users is accessible. Moreover, the attacker can apply advanced attacks, such as map matching and semantic location attacks. In response to these issues, this work employs artificial intelligence to build a robust defense against advanced location privacy attacks. The key idea behind protecting the location privacy of LBS users is to generate smart dummy locations. Smart dummy locations are false locations with the same query probability as the real location, but they are far from both the real location and each other. Relying on the previous two conditions, the deep-learning-based intelligent finder ensures a high level of location privacy protection against advanced attacks. The attacker cannot recognize the dummies from the real location and cannot isolate the real location by a filtering process. In terms of entropy (the privacy protection metric), accuracy (the deep learning metric), and total execution time (the performance metric) and compared to the well-known DDA and BDA systems, the proposed system shows better results, where entropy = 15.9, accuracy = 9.9, and total execution time = 17 sec.

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“…Information Retrieval (PIR) [25] was used to achieve full privacy protection. The key idea of the PIR technique depends on mathematical principle.…”

Section: Related Workmentioning

confidence: 99%

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Alyousef¹,

Srinivasan²,

Alrahhal³

et al. 2022

IJACSA

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“…A multi-server private information retrieval-based scheme has been proposed to preserve the location privacy of the stationary and mobile incumbents and CBSDs in [73,74]. Private information retrieval allows the retrieval of the required information from a database, preserving the identity of the retrieving user from the database owner.…”

Section: Cryptography-based Schemesmentioning

confidence: 99%

A Survey on Citizens Broadband Radio Service (CBRS)

et al. 2022

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To leverage the existing spectrum and mitigate the global spectrum dearth, the Federal Communications Commission of the United States has recently opened the Citizens Broadband Radio Service (CBRS) spectrum, spanning 3550–3700 MHz, for commercial cognitive operations. The CBRS has a three-tier hierarchical architecture, wherein the incumbents, including military radars, occupy the topmost tier. The priority access licenses (PAL) and general authorized access (GAA) are second and third tier, respectively, facilitating licensed and unlicensed access to the spectrum. This combination of licensed and unlicensed access to the spectrum in a three-tier model has opened novel research directions in optimal spectrum sharing as well as privacy preservation, and hence, several schemes have been proposed for the same. This article provides a detailed survey of the existing literature on the CBRS. We provide an overview of the CBRS ecosystem and discuss the regulation and standardization process and industrial developments on the CBRS. The existing schemes for optimal spectrum sharing and resource allocation in CBRS are discussed in detail. Further, an in-depth study of the existing literature on the privacy of incumbents, PAL devices, and GAA devices in CBRS is presented. Finally, we discuss the open issues in CBRS, which demand more attention and effort.

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“…Instead of storing encrypted offers, receiving encrypted requests from the users, and matching the encrypted requests with the encrypted offers, it is more efficient to store the data in the system without encryption, and allow the user to use a PIR technique to privately retrieve the required offers. The PIR techniques have been used in different applications, such as location-based services [35] and eHealth systems [36]. In location-based services, the PIR is used to enable a user to privately query a server to retrieve location information about his/her point of interests (e.g., locations of entertainment centers, shopping malls, restaurants, etc.).…”

Section: B Private Information Retrieval (Pir)mentioning

confidence: 99%

“…Badr et al: Smart Parking System with Privacy-preservation and Reputation using Blockchain C [1] C [2] C [3] C [4] C [5] C [6] C [7] C [8] C [9] C [10] C [11] C [12] C [13] C [14] C [15] C [16] C [17] C [18] C [19] C [20] C [21] C [22] C [23] C [24] C [25] C [26] C [27] C [28] C [29] C [30] C [31] C [32] C [33] C [34] C [35]…”

Section: B System Initializationmentioning

confidence: 99%

Smart Parking System With Privacy Preservation and Reputation Management Using Blockchain

et al. 2020

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Most of the existing smart parking systems threaten the drivers' privacy by revealing information about their visited locations. Moreover, they are centralized making them vulnerable to a single point of failure and attack, which threatens the availability of the parking service. They also suffer from a lack of transparency, where the centralized service organizer may favor some parking lots by booking their parking slots first. To tackle these concerns, we propose a blockchain-based smart parking system with privacy preservation and reputation management. In our system, a consortium blockchain is created by different parking lots to run the parking system securely and transparently, where the parking offers are recorded on a shared and immutable ledger. We use a commitment technique during the submission of the offers to ensure fair parking rates. Then, we use a private information retrieval technique during the offers retrieval to preserve the drivers' location privacy. Furthermore, to anonymously and efficiently authenticate drivers during the reservation process, we use a short randomizable signature. We also use a time-locked anonymous payment technique to discourage drivers from not committing to their parking reservations and provide a secure and privacy-preserving payment method for parking service. Finally, we integrate a blockchain-based anonymous reputation management scheme into our system, where drivers can anonymously rate the parking service to ensure high quality of service. Our evaluations demonstrate that our smart parking system is secure and capable of preserving drivers' privacy with low communication, computation, and storage overheads.

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Location Privacy in Cognitive Radios With Multi-Server Private Information Retrieval

Cited by 16 publications

References 43 publications

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

A Survey on Citizens Broadband Radio Service (CBRS)

Smart Parking System With Privacy Preservation and Reputation Management Using Blockchain

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