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Spatial Data Publication Under Local Differential Privacy

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Web Information Systems and Applications (WISA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13579))

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Abstract

Local differential privacy (LDP), which has been applied in Google Chrome and Apple iOS, provides strong privacy assurance to users when collecting data from users. We focus on the sensitive spatial data collection, with the goal of obtaining high result utility while satisfying LDP. The existing methods for this problem mostly target at the task of range queries. They combine the frequency estimation technology and spatial decomposition method to publish the number of users located in some sub-spaces. However, these methods cannot well support distance-related applications such as k-means clustering, since they treat the sub-spaces not containing the user equally and do not consider the distances between the sub-space and user data. Motivated by this, we propose dimension-correlated piecewise mechanism (DCPM), a novel LDP perturbation mechanism with a well-designed probability density, in which the distance between the published value and the true one is considered. Extensive experiments on real-world data and synthetic data demonstrate that DCPM achieves significantly higher result utility compared to previous solutions.

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References

  1. Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: CCS, pp. 1054–1067. ACM (2014)

    Google Scholar 

  2. Ding, B., Kulkarni, J., Yekhanin, S.: Collecting telemetry data privately. In: NIPS, pp. 3571–3580 (2017)

    Google Scholar 

  3. Liu, Y., et al.: Differentially private linear regression analysis via truncating technique. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds.) WISA 2021. LNCS, vol. 12999, pp. 249–260. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87571-8_22

    Chapter  Google Scholar 

  4. Zhang, J., Xiao, X., Xie, X.: Privtree: a differentially private algorithm for hierarchical decompositions. In: SIGMOD Conference, pp. 155–170. ACM (2016)

    Google Scholar 

  5. Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? SIAM J. Comput. 40(3), 793–826 (2011)

    Article  MATH  Google Scholar 

  6. Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. In: FOCS, pp. 429–438. IEEE Computer Society (2013)

    Google Scholar 

  7. Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60, 63–66 (1965)

    Article  MATH  Google Scholar 

  8. Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: USENIX Security Symposium. pp. 729–745. USENIX Association (2017)

    Google Scholar 

  9. Wang, S., Li, J., Qian, Y., Du, J., Lin, W., Yang, W.: Hiding numerical vectors in local private and shuffled messages. In: IJCAI, pp. 3706–3712. ijcai.org (2021)

    Google Scholar 

  10. Ye, Q., Hu, H.: Local differential privacy: tools, challenges, and opportunities. In: U, L.H., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds.) WISE 2020. CCIS, vol. 1155, pp. 13–23. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3281-8_2

    Chapter  Google Scholar 

  11. Yang, M., Lyu, L., Zhao, J., Zhu, T., Lam, K.: Local differential privacy and its applications: a comprehensive survey. CoRR abs/2008.03686 (2020)

    Google Scholar 

  12. De Berg, M.T., Van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-662-04245-8

    Book  MATH  Google Scholar 

  13. Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco (2006)

    MATH  Google Scholar 

  14. Cormode, G., Kulkarni, T., Srivastava, D.: Answering range queries under local differential privacy. Proc. VLDB Endow. 12(10), 1126–1138 (2019)

    Article  Google Scholar 

  15. Yang, J., Wang, T., Li, N., Cheng, X., Su, S.: Answering multi-dimensional range queries under local differential privacy. Proc. VLDB Endow. 14(3), 378–390 (2020)

    Article  Google Scholar 

  16. Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: ICDE, pp. 289–300. IEEE Computer Society (2016)

    Google Scholar 

  17. Bassily, R., Smith, A.D.: Local, private, efficient protocols for succinct histograms. In: STOC, pp. 127–135. ACM (2015)

    Google Scholar 

  18. Jorgensen, Z., Yu, T., Cormode, G.: Conservative or liberal? personalized differential privacy. In: ICDE, pp. 1023–1034. IEEE Computer Society (2015)

    Google Scholar 

  19. Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)

    MATH  Google Scholar 

  20. Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14

    Chapter  Google Scholar 

  21. Duchi, J.C., Wainwright, M.J., Jordan, M.I.: Minimax optimal procedures for locally private estimation. CoRR abs/1604.02390 (2016)

    Google Scholar 

  22. Nguyên, T.T., Xiao, X., Yang, Y., Hui, S.C., Shin, H., Shin, J.: Collecting and analyzing data from smart device users with local differential privacy. CoRR abs/1606.05053 (2016)

    Google Scholar 

  23. Geng, Q., Kairouz, P., Oh, S., Viswanath, P.: The staircase mechanism in differential privacy. IEEE J. Sel. Top. Signal Process. 9(7), 1176–1184 (2015)

    Article  Google Scholar 

  24. Wang, N., et al..: Collecting and analyzing multidimensional data with local differential privacy. In: ICDE, pp. 638–649. IEEE (2019)

    Google Scholar 

  25. Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: KDD, pp. 316–324. ACM (2011)

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61902365 and 61902366), Open Project Program from Key Lab of Cryptologic Technology and Information Security, Ministry of Education, Shandong University, and the Fundamental Research Funds for the Central Universities (202042008).

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Authors and Affiliations

  1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China

    Jian Zhuang, Ning Wang, Zhigang Wang, Xiaodong Wang, Haipeng Qu & Zhiqiang Wei

  2. Key Lab of Cryptologic Technology and Information Security, Ministry of Education, Shandong University, Qingdao, China

    Ning Wang

Authors
  1. Jian Zhuang
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  2. Ning Wang
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  3. Zhigang Wang
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  4. Xiaodong Wang
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  5. Haipeng Qu
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  6. Zhiqiang Wei
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Corresponding author

Correspondence to Ning Wang .

Editor information

Editors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Xiang Zhao

  2. Guangzhou University, Guangzhou, China

    Shiyu Yang

  3. Tianjin University, Tianjin, China

    Xin Wang

  4. Deakin University, Melbourne, VIC, Australia

    Jianxin Li

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Zhuang, J., Wang, N., Wang, Z., Wang, X., Qu, H., Wei, Z. (2022). Spatial Data Publication Under Local Differential Privacy. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_55

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  • DOI: https://doi.org/10.1007/978-3-031-20309-1_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20308-4

  • Online ISBN: 978-3-031-20309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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