ProCSA: Protecting Privacy in Crowdsourced Spectrum Allocation

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Abstract

Sharing a spectrum is an emerging paradigm to increase spectrum utilization and thus address the unabated increase in mobile data consumption. The paradigm allows the “unused” spectrum bands of licensed primary users to be shared with secondary users, as long as the allocated spectrum to the secondary users does not cause any harmful interference to the primary users. However, such shared spectrum paradigms pose serious privacy risks to the participating entities, e.g., the secondary users may be sensitive about their locations and usage patterns. This paper presents a privacy-preserving protocol for the shared spectrum allocation problem in a crowdsourced architecture, wherein spectrum allocation to secondary users is done based on real-time sensing reports from geographically distributed and crowdsourced spectrum sensors. Such an architecture is highly desirable since it obviates the need to assume a propagation model, and facilitates estimation based on real-time propagation conditions and high granularity data via inexpensive means. We design our protocol by leveraging the efficiency and generality of recently developed fast and secure two-party computation (S 2 PC ) protocols. We show that this approach leads to practical solutions that outperform the state-of-the-art in terms of both efficiency as well as functionality. To achieve the desired computational efficiency, we optimize the spectrum allocation algorithm to select a small number of relevant reports based on certain parameters. This results in a faster RAM program for power allocation which, under suitable adjustments to underlying arithmetic operations, can be efficiently implemented using S 2 PC. We use the standard “ideal/real paradigm” to define the security of spectrum allocation and prove security of our protocol (in the semi-honest model). We also provide data from extensive simulations to demonstrate the accuracy, as well as computational and communication efficiency of our schemes.

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APA

Curran, M., Liang, X., Gupta, H., Pandey, O., & Das, S. R. (2019). ProCSA: Protecting Privacy in Crowdsourced Spectrum Allocation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11735 LNCS, pp. 556–576). Springer. https://doi.org/10.1007/978-3-030-29959-0_27

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