Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures

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

doi:10.1109/tccn.2017.2702163

Cited by 26 publications

(14 citation statements)

References 34 publications

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“…In this way, they hide the actual locations of users with the support of a fast location query. In cognitive radio networks, geo-location database-driven methods are proposed to identify vacant frequency bands for the secondary users without harm the primary users [52] [53]. In these methods, the secondary users are equipped with GPS devices to query the geo-location database.…”

Section: Privacy Preservationmentioning

confidence: 99%

“…net. [52] [53] Location anonymization CQ, CA LBSs [17] Location anonymization CQ, CA Biometrics [27] [26] Biometrics info. anonymization CQ, CA MSNs [56] Interested topics anonymization CQ, CA Record linkage [55] Entry anonymization CQ, CA CDRs [57] Record anonymization CQ, CA Smart grid [63] Representing CRL list CQ, SS BF is a popular choice in designing privacy-friendly solutions.…”

Section: Scenariosmentioning

confidence: 99%

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Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

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Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

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Section: Privacy Preservationmentioning

confidence: 99%

Section: Scenariosmentioning

confidence: 99%

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

View full text Add to dashboard Cite

show abstract

“…PIR technology can protect the privacy of SUs' locations well, but it also causes high communication and computational overheads. Grissa et al [21] proposed a cuckoo filter-based protocol: the DB uses a cuckoo filter to compress the spectrum information and send it to the query server. The SUs then obtain the available channels through the query server.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we evaluate the performance of our proposed protocol, by showing its computation and communication costs, and compare them with the protocols proposed by Grissa et al [21] and Gao et al [20]. All experiments have been conducted on a 64-bit computer with an Intel Core i7 CPU of 2.5 GHz and 16G memory.…”

Section: Performance Evaluationmentioning

confidence: 99%

Lightweight Privacy Preservation for Securing Large-Scale Database-Driven Cognitive Radio Networks with Location Verification

Zhu

Zeng

et al. 2019

Security and Communication Networks

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The database-driven cognitive radio networks (CRNs) are regarded as a promising approach to utilizing limited spectrum resources in large-scale Internet of Things (IoT). However, database-driven CRNs face some security and privacy threats. Firstly, secondary users (SUs) should send identity and location information to the database (DB) to obtain a list of available channels, such that the curious DB might easily misuse and threaten the privacy of SUs. Secondly, malicious SUs might send fake location information to the DB in order to occupy channels with better quantity in advance and so gain benefits. This might also cause serious interference to primary users (PUs). In this paper, we propose a lightweight privacy-preserving location verification protocol to protect the identity and location privacy of each SU and to verify the location of SUs. In the proposed protocol, the SU does not need to provide location information to request an available channel from the DB. Therefore, the DB cannot get the location information of any SU. In the proposed protocol, the base station (BS) selects some SUs as witnesses to generate location proofs for each other in a distributed fashion. This new witness selection mechanism makes the proposed protocol reliable when a malicious SU generates fake location information to cheat the BS and also prevents SU-Witness collusion attacks. The results also show that the proposed protocol can provide strong privacy preservation for SUs and can effectively verify the location of the SUs. The security analysis shows that the proposed protocol can resist various types of attacks. Moreover, compared with previous protocols, the proposed protocol is lightweight because it relies on symmetric cryptography and it is unaffected by the area covered by the DB.

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“…Furthermore, it has been shown in a recent study led by Sprint and Technicolor [25] that anonymization based techniques are not efficient in providing location privacy guarantees, and may even leak some location information. Grissa et al [21], [54] propose an information theoretic approach which could be considered as a variant of the trivial PIR solution. They achieve this by using setmembership probabilistic data structures/filters to compress the content of the database and send it to SU which then needs to try several combinations of channels and transmission parameters to check their existence in the data structure.…”

Section: Related Workmentioning

confidence: 99%

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

Grissa

Yavuz

Hamdaoui

2019

IEEE Trans. Cogn. Commun. Netw.

Self Cite

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Spectrum database-based cognitive radio networks (CRN s) have become the de facto approach for enabling unlicensed secondary users (SU s) to identify spectrum vacancies in channels owned by licensed primary users (PU s). Despite its merits, the use of spectrum databases incurs privacy concerns for both SU s and PU s. Single-server private information retrieval (PIR) has been used as the main tool to address this problem. However, such techniques incur extremely large communication and computation overheads while offering only computational privacy. Besides, some of these PIR protocols have been broken.In this paper, we show that it is possible to achieve high efficiency and (information-theoretic) privacy for both PU s and SU s in database-driven CRN with multi-server PIR. Our key observation is that, by design, database-driven CRN s comprise multiple databases that are required, by the Federal Communications Commission, to synchronize their records. To the best of our knowledge, we are the first to exploit this observation to harness multi-server PIR technology to guarantee an optimal privacy for both SU s and PU s, thanks to the unique properties of database-driven CRN . We showed, analytically and empirically with deployments on actual cloud systems, that multi-server PIR is an ideal tool to provide efficient location privacy in database-driven CRN .Index Terms-Database-driven cognitive radio networks, location privacy, dynamic spectrum access, private information retrieval.

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Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures

Cited by 26 publications

References 34 publications

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Lightweight Privacy Preservation for Securing Large-Scale Database-Driven Cognitive Radio Networks with Location Verification

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

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