Introduction

  • Wei Wang
  • Qian Zhang
Chapter
First Online:
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

The rapid proliferation of wireless technology offers the promise of many societal and individual benefits by enabling pervasive networking and communication via personal devices such as smartphones, PDAs, computers. This explosion of wireless devices and mobile data creates an ever-increasing demand for more radio spectrum. The spectrum scarcity issue is expected to occur due to the limited spectrum resources. However, previous studies [15] have shown that the usage of many spectrum bands (e.g., UHF bands) is inefficient, which motivates the concept of cognitive radio networks (CRNs) [22, 24, 25]. In CRNs, secondary (unlicensed) users (SUs) are allowed to access licensed spectrum bands given that it only incurs minimal tolerable or no interference to primary (licensed) users (PUs).

Keywords

Cognitive Radio Network Fusion Center Location Privacy Privacy Preserve Sequential Probability Ratio Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 2.
    CogNeA: Cognitive Networking Alliance. http://www.cognea.org.
  2. 3.
    IEEE 802.22 WRAN WG on Broadband Wireless Acess Standards. http://www.ieee802.org/22.
  3. 5.
    R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD), 2000.Google Scholar
  4. 6.
    H. Ahmadi, N. Pham, R. Ganti, T. Abdelzaher, S. Nath, and J. Han. Privacy-aware regression modeling of participatory sensing data. In Proc. ACM Conference on Embedded Networked Sensor Systems (SenSys), 2010.Google Scholar
  5. 11.
    E. Beinat. Privacy and location-based: Stating the policies clearly. In GeoInformatics, pages 14–17, 2001.Google Scholar
  6. 12.
    A. Blum, K. Ligett, and A. Roth. A learning theory approach to non-interactive database privacy. In Proc. ACM STOC, 2008.Google Scholar
  7. 13.
    D. Cabric, S.M. Mishra, and R.W. Brodersen. Implementation issues in spectrum sensing for cognitive radios. In Proc. IEEE Asilomar conf, Signals, Systems, and Computers, volume 1, pages 772–776, 2004.Google Scholar
  8. 15.
    D. Chen, S. Yin, Q. Zhang, M. Liu, and S. Li. Mining spectrum usage data: a large-scale spectrum measurement study. In Proc. ACM International Conference on Mobile Computing and Networking (MobiCom), pages 13–24, 2009.Google Scholar
  9. 16.
    H. Chen and W. Gao. Btext on cyclostationary feature detector - information annex on sensing techniques. IEEE 802.22 Meeting Doc., 2007.Google Scholar
  10. 17.
    R. Chen, N. Mohammed, B.C.M. Fung, B.C. Desai, and L. Xiong. Publishing set-valued data via differential privacy. International Conference on Very Large Databases (VLDB), 4(11), 2011.Google Scholar
  11. 21.
    T.C. Clancy. Formalizing the interference temperature model. Wireless Communications and Mobile Computing, 7(9):1077–1086, 2007.CrossRefGoogle Scholar
  12. 22.
    Federal Communications Commission. Spectrum policy task force report. ET Docket No. 02-135, 2002.Google Scholar
  13. 23.
    Federal Communications Commission. Establishment of interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed mobile and satellite frequency bands. ET Docket No. 03-289, 2003.Google Scholar
  14. 24.
    Federal Communications Commission. Facilitating opportunities for flxible, efficient, and reliable spectrum use employing cognitive radio technologies. ET Docket No. 03-108, 2003.Google Scholar
  15. 25.
    Federal Communications Commission. Notice of proposed rule making and order. ET Docket No. 03-322, 2003.Google Scholar
  16. 26.
    Federal Communications Commission. Second report and order. FCC 08-260, Nov. 2008.Google Scholar
  17. 27.
    Federal Communications Commission. Second memorandum opinion and order. FCC 10-174, 2010.Google Scholar
  18. 28.
    Federal Communications Commission. Third memorandum opinion and order. FCC 12-36, May 2012.Google Scholar
  19. 31.
    F.F. Digham, M.S. Alouini, and M.K. Simon. On the energy detection of unknown signals over fading channels. IEEE Trans. Commun., 55(1):21–24, 2007.CrossRefGoogle Scholar
  20. 34.
    C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. pages 265–284, 2006.Google Scholar
  21. 37.
    X. Feng, J. Zhang, and Q. Zhang. Database-assisted multi-ap network on tv white spaces: Architecture, spectrum allocation and ap discovery. In Proc. IEEE New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pages 265–276, 2011.Google Scholar
  22. 38.
    A. Friedman and A. Schuster. Data mining with differential privacy. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010.Google Scholar
  23. 39.
    Z. Gao, H. Zhu, Y. Liu, M. Li, and Z. Cao. Location privacy leaking from spectrum utilization information in database-driven cognitive radio network. In Proceedings of the 2012 ACM conference on Computer and communications security, Proc. ACM Computer and communications security, 2012.Google Scholar
  24. 40.
    Z. Gao, H. Zhu, Y. Liu, M. Li, and Z. Cao. Location privacy in database-driven cognitive radio networks: Attacks and countermeasures. In Proc. IEEE International Conference on Computer Communications (INFOCOM), 2013.Google Scholar
  25. 42.
    B. Gedik and L. Liu. Location privacy in mobile systems: A personalized anonymization model. In Proc. IEEE International Conference on Distributed Computing Systems (ICDCS), 2005.Google Scholar
  26. 43.
    A. Ghasemi and E.S. Sousa. Opportunistic spectrum access in fading channels through collaborative sensing. J. Commun., 2(2):71–82, 2007.CrossRefGoogle Scholar
  27. 44.
    J. Girao, D. Westhoff, and M. Schneider. Cda: Concealed data aggregation for reverse multicast traffic in wireless sensor networks. In Proc. IEEE International Conference on Communications (ICC), 2005.Google Scholar
  28. 47.
    M.M. Groat, W. He, and S. Forrest. KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks. In Proc. IEEE International Conference on Computer Communications (INFOCOM), 2011.Google Scholar
  29. 50.
    S. Haykin, D.J. Thomson, and J.H. Reed. Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5):849–877, 2009.CrossRefGoogle Scholar
  30. 51.
    B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady. Enhancing security and privacy in traffic-monitoring systems. IEEE Perv. Comput., 5(4):38–46, 2006.CrossRefGoogle Scholar
  31. 53.
    J. Jia, Q. Zhang, and X. Shen. Hc-mac: A hardware-constrained cognitive mac for efficient spectrum management. IEEE J. Sel. Areas Commun., 26(1):106–117, 2008.CrossRefGoogle Scholar
  32. 54.
    J. Jia, Q. Zhang, Q. Zhang, and M. Liu. Revenue generation for truthful spectrum auction in dynamic spectrum access. In Proc. ACM symposium on Mobile ad hoc networking and computing (MobiHoc), pages 3–12, 2009.Google Scholar
  33. 56.
    V. Kindratenko, M. Pant, and D. Pointer. Deploying the olsr protocol on a network using sdr as the physical layer. Technical report, NCASSR Technical Report, UCLA, 2005.Google Scholar
  34. 60.
    K. Letaief and W. Zhang. Cooperative communications for cognitive radio networks. Proceedings of the IEEE, 97(5):878–893, 2009.CrossRefGoogle Scholar
  35. 64.
    S. Li, H. Zhu, Z. Gao, X. Guan, K. Xing, and X. Shen. Location privacy preservation in collaborative spectrum sensing. In Proc. IEEE International Conference on Computer Communications (INFOCOM), 2012.Google Scholar
  36. 65.
    B. Liu, Y. Jiang, F. Sha, and R. Govindan. Cloud-enabled privacy-preserving collaborative learning for mobile sensing. In Proc. ACM Conference on Embedded Networked Sensor Systems (SenSys), 2012.Google Scholar
  37. 68.
    Y. Matsuo, N. Okazaki, K. Izumi, Y. Nakamura, T. Nishimura, K. Hasida, and H. Nakashima. Inferring long-term user properties based on users’ location history. In Proc. International Joint Conference on Artificial Intelligence (IJCAI), pages 2159–2165, 2007.Google Scholar
  38. 71.
    Microsoft. Location based services usage and perceptions survey. 2011.Google Scholar
  39. 81.
    B. Przydatek, D. Song, and A. Perrig. Sia: Secure information aggregation in sensor networks. In Proc. ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003.Google Scholar
  40. 86.
    P. Samarati. Protecting respondents identities in microdata release. IEEE Trans. on Knowl. and Data Eng., 2001.Google Scholar
  41. 87.
    S.J. Shellhammer, S.Sh. N, R. Tandra, and J. Tomcik. Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs. In Proc. ACM International Workshop on Technology and Policy for accessing Spectrum (TAPAS), 2006.Google Scholar
  42. 88.
    M. Shin, C. Cornelius, D. Peebles, A. Kapadia, D. Kotz, and N. Triandopoulos. Anonysense: A system for anonymous opportunistic sensing. J. Perv. Mobile Comput., 7(1):16–30, 2011.Google Scholar
  43. 89.
    L. Sweeney. k-anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 2002.Google Scholar
  44. 90.
    A. Taherpour, Y. Norouzi, M. Nasiri-Kenari, A. Jamshidi, and Z. Zeinalpour-Yazdi. Asymptotically optimum detection of primary user in cognitive radio networks. Communications, IET, 1(6):1138–1145, 2007.CrossRefGoogle Scholar
  45. 91.
    K. Tan, H. Liu, J. Zhang, Y. Zhang, J. Fang, and G.M. Voelker. Sora: high-performance software radio using general-purpose multi-core processors. Comm. ACM, 54(1):99–107, 2011.CrossRefGoogle Scholar
  46. 92.
    R. Tandra and A. Sahai. Snr walls for signal detection. IEEE J. Sel. Topics in Signal Processing, 2(1):4–17, 2008.CrossRefGoogle Scholar
  47. 93.
    C. Tang. Obama administration calls for “privacy bill of rights”. 2011.Google Scholar
  48. 101.
    P.K. Varshney and C.S. Burrus. Distributed detection and data fusion. Springer-Verlag New York, Inc., 1997.CrossRefGoogle Scholar
  49. 102.
    K. Vu, R. Zheng, and J. Gao. Efficient algorithms for k-anonymous location privacy in participatory sensing. In Proc. IEEE International Conference on Computer Communications (INFOCOM), 2012.Google Scholar
  50. 103.
    A. Wald. Sequential tests of statistical hypotheses. Ann. Math. Statist., 16(2):117–186, 1945.CrossRefMATHMathSciNetGoogle Scholar
  51. 106.
    S.B. Wicker. The loss of location privacy in the cellular age. Commun. ACM, 55(8):60–68, 2012.CrossRefGoogle Scholar
  52. 111.
    J. Xu, Z. Zhang, X. Xiao, Y. Yang, and G. Yu. Differentially private histogram publication. In Proc. IEEE International Conference on Data Engineering (ICDE), 2012.Google Scholar
  53. 117.
    Q. Zhang, J. Jia, and J. Zhang. Cooperative relay to improve diversity in cognitive radio networks. Communications Magazine, IEEE, 47(2):111–117, 2009.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Wei Wang
    • 1
  • Qian Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyKowloonHong Kong SAR

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