Location Privacy in Cognitive Radio Networks: A Survey

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

doi:10.1109/comst.2017.2693965

Cited by 49 publications

(25 citation statements)

References 249 publications

(430 reference statements)

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“…Moreover, the work of this survey paper is also related to the survey papers on various aspects of CRNs, such as spectrum sensing [25]- [30], spectrum decision [31]- [35], resource allocation [36]- [41], security and privacy [42]- [48], MAC protocols [49], [50], routing [51], network coding [52], various applications [53]- [56], to just mention a few. A list of the related survey papers is provided in Table I for further references.…”

Section: B Comparison With Related Survey Articlesmentioning

confidence: 99%

“…2015 [47] A survery of the recent advances on security threats/attacks and countermeasures in CRNs focusing more on the physical layer. 2017 [48] A survey that investigates the various location privacy risks and threats that may arise from the different components of this CRN technology, and explores the different privacy attacks and countermeasure solutions.…”

Section: Spectrum Sensingmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

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Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

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Section: B Comparison With Related Survey Articlesmentioning

confidence: 99%

Section: Spectrum Sensingmentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

View full text Add to dashboard Cite

show abstract

“…Despite its importance, the location privacy issue in CRN s only recently gained interest from the research community [12]. Some works focused on addressing this issue in the context of collaborative spectrum sensing [13]- [17] while others focused on addressing it in the context of dynamic spectrum auction [18].…”

Section: Related Workmentioning

confidence: 99%

“…However, many of these PETs are designed for applications that do not fit in the context of CRN s. For instance, oblivious random access memory (ORAM) [29] aims to enable a user to outsource its encrypted data to a database and to offer him/her the possibility to access this data while hiding the access patterns from the database [12]. Searchable symmetric encryption [30] is another PET that is largely deployed to privately outsource one's data to another party while maintaining the ability to selectively search over it [12]. These PETs are designed for protecting queries and searches over data that is outsourced to a database, which is completely different from the CRN scenario where the queried data belong to the database itself.…”

Section: Related Workmentioning

confidence: 99%

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

Grissa

Yavuz

Hamdaoui

2017

IEEE Trans. Cogn. Commun. Netw.

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In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks (CRN s) that protect secondary users' (SU s) location privacy while allowing them to learn spectrum availability in their vicinity. Our schemes harness probabilistic set membership data structures to exploit the structured nature of spectrum databases (DB s) and SU s' queries. This enables us to create a compact representation of DB that could be queried by SU s without having to share their location with DB , thus guaranteeing their location privacy. Our proposed schemes offer different costperformance characteristics. Our first scheme relies on a simple yet powerful two-party protocol that achieves unconditional security with a plausible communication overhead by making DB send a compacted version of its content to SU which needs only to query this data structure to learn spectrum availability. Our second scheme achieves significantly lower communication and computation overhead for SU s, but requires an additional architectural entity which receives the compacted version of the database and fetches the spectrum availability information in lieu of SU s to alleviate the overhead on the latter. We show that our schemes are secure, and also demonstrate that they offer significant advantages over existing alternatives for various performance and/or security metrics.Index Terms-Database-driven spectrum availability, location privacy preservation, cognitive radio networks, set membership data structures.

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“…In the context of Cognitive Radio Networks (CRNs), Grissa and al. [42] provide a comprehensive survey that investigates the various location privacy risks and threats as well, as countermeasures that have been proposed in the literature to cope with these location privacy issues. Lastly, Cottrill [43] uses a multidisciplinary approach to discuss location privacy and to ensure that the protection of private information is directly addressed from each of the relevant standpoints of law, policy, and technology.…”

Section: Introductionmentioning

confidence: 99%

The Long Road to Computational Location Privacy: A Survey

Primault

Boutet

Mokhtar

et al. 2019

IEEE Commun. Surv. Tutorials

128

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The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed.

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Location Privacy in Cognitive Radio Networks: A Survey

Cited by 49 publications

References 249 publications

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

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

The Long Road to Computational Location Privacy: A Survey

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