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A privacy-aware monitoring algorithm for moving \(k\)-nearest neighbor queries in road networks

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Abstract

Location privacy is a major obstacle in the ubiquitous deployment of mobile and pervasive computing services. In this study, we present a new approach for preserving the trajectory privacy of moving \(k\)-nearest neighbor (M\(k\)NN) queries in road networks. Several location anonymization algorithms have been proposed for providing location privacy to users traveling on a road network. These algorithms focus primarily on the location anonymization of snapshot queries. Indeed, users move freely and arbitrarily, and thus query results provided to them soon become invalid as their locations change. To refresh the query result, each user must therefore periodically contact the location-based service, enabling attackers to identify and track the user easily. In addition, frequent location updates for the user may incur severe computational and communication costs. We address these issues by proposing a privacy-aware monitoring algorithm, called PAMA, for preserving the trajectory privacy of M\(k\)NN queries in road networks. Our simulation results show that PAMA significantly outperforms conventional algorithms in terms of both security and performance.

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Acknowledgments

We thank the anonymous reviewers for their very useful comments and suggestions. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2012R1A1A2043422).

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Authors and Affiliations

  1. School of Computer Information, Kyungpook National University Sangju Campus, Gajang-dong, Sangju-si, Gyeongsangbuk-do,  742-711, South Korea

    Hyung-Ju Cho

  2. Department of Information & Computer Engineering, Ajou University, Woncheon-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-749, South Korea

    Se Jin Kwon, Rize Jin & Tae-Sun Chung

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  1. Hyung-Ju Cho
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  2. Se Jin Kwon
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  3. Rize Jin
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Correspondence to Hyung-Ju Cho.

Appendices

Appendix 1

Here we present the standard deviations for the tracking confidence of the adversary. In summary, \(\text {PAMA}^{+}\) and XStar show the smallest and largest standard deviations for the tracking confidence, respectively (See Tables 7, 8, 9, 10, 11 and 12).

Table 7 Effect of \(N_{POI}\)
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Table 8 Effect of \(N_{qry}\)
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Table 9 Effect of \(k\)
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Table 10 Effect of \(V_{qry}\)
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Table 11 Effect of \(R_c =\langle \delta _m ,\delta _l\rangle \)
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Table 12 Effect of distributions \(\langle D_{POI} ,D_{qry}\rangle \)
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Appendix 2

Here we present the standard deviations for query processing time. In summary, \(\text {PAMA}^{+}\) and XStar show the smallest and largest standard deviations for the query processing time, respectively (See Tables 13, 14, 15, 16, 17 and 18).

Table 13 Effect of \(N_{POI}\)
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Table 14 Effect of \(N_{qry}\)
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Table 15 Effect of \(k\)
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Table 16 Effect of \(V_{qry}\)
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Table 17 Effect of \(R_c =\langle \delta _m ,\delta _l\rangle \)
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Table 18 Effect of distributions \(\langle D_{POI} ,D_{qry}\rangle \)
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Cho, HJ., Kwon, S.J., Jin, R. et al. A privacy-aware monitoring algorithm for moving \(k\)-nearest neighbor queries in road networks. Distrib Parallel Databases 33, 319–352 (2015). https://doi.org/10.1007/s10619-014-7152-z

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