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Distributed Differentially Private Ranking Aggregation

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Advances in Knowledge Discovery and Data Mining (PAKDD 2022)

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Abstract

Ranking aggregation is commonly adopted in cooperative decision-making to assist combining multiple rankings into a single representative. To protect the actual ranking of each individual, some privacy-preserving strategies, such as differential privacy, are often used. This, however, does not consider the scenario where the curator, who collects all rankings from individuals, is untrustworthy. This paper proposed a mechanism to solve the above issue using the distribute differential privacy framework. The proposed mechanism collects locally differential private rankings from individuals, then randomly permutes pairwise rankings using a shuffle model to further amplify the privacy protection. The final representative is produced by hierarchical rank aggregation. The mechanism was theoretically analysed and experimentally compared against existing methods, and demonstrated competitive results in both the output accuracy and privacy protection.

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References

  1. Bittau, A., et al.: Prochlo: strong privacy for analytics in the crowd. In: Proceedings of the 26th Symposium on Operating Systems Principles, pp. 441–459 (2017)

    Google Scholar 

  2. Black, D., et al.: The theory of committees and elections (1958)

    Google Scholar 

  3. Cheu, A., Smith, A., Ullman, J., Zeber, D., Zhilyaev, M.: Distributed differential privacy via shuffling. In: Ishai, Y., Rijmen, V. (eds.) EUROCRYPT 2019. LNCS, vol. 11476, pp. 375–403. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17653-2_13

    Chapter  Google Scholar 

  4. Ding, J., Han, D., Dezert, J., Yang, Y.: A new hierarchical ranking aggregation method. Inf. Sci. 453, 168–185 (2018)

    Article  MathSciNet  Google Scholar 

  5. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1

    Chapter  Google Scholar 

  6. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622 (2001)

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  8. Eppstein, D.: Instability vs anonymization in e pluribus hugo (2015). https://11011110.github.io/blog/2015/09/09/instability-vs-anonymization.html

  9. Hay, M., Elagina, L., Miklau, G.: Differentially private rank aggregation. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 669–677. SIAM (2017)

    Google Scholar 

  10. Hoare, C.A.: Quicksort. Comput. J. 5(1), 10–16 (1962)

    Article  MathSciNet  Google Scholar 

  11. Kendall, M.G.: Rank correlation methods (1948)

    Google Scholar 

  12. Mallows, C.L.: Non-null ranking models. I. Biometrika 44(1/2), 114–130 (1957)

    Article  MathSciNet  Google Scholar 

  13. Mao, A., Procaccia, A., Chen, Y.: Better human computation through principled voting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 27 (2013)

    Google Scholar 

  14. Meinhardt, H.I.: Cooperative Decision Making in Common Pool Situations, vol. 517. Springer, Heidelberg (2012)

    MATH  Google Scholar 

  15. Narayan, A.: Distributed differential privacy and applications. University of Pennsylvania (2015)

    Google Scholar 

  16. Shang, S., Wang, T., Cuff, P., Kulkarni, S.: The application of differential privacy for rank aggregation: privacy and accuracy. In: 17th International Conference on Information Fusion (FUSION), pp. 1–7. IEEE (2014)

    Google Scholar 

  17. Wang, S., et al.: Aggregating votes with local differential privacy: usefulness, soundness vs. indistinguishability. arXiv preprint arXiv:1908.04920 (2019)

  18. Yan, Z., Li, G., Liu, J.: Private rank aggregation under local differential privacy. Int. J. Intell. Syst. 35(10), 1492–1519 (2020)

    Article  Google Scholar 

  19. Zhu, T., Li, G., Zhou, W., Philip, S.Y.: Differentially private data publishing and analysis: a survey. IEEE Trans. Knowl. Data Eng. 29(8), 1619–1638 (2017)

    Article  Google Scholar 

Download references

Acknowledgement

This research is supported by the National Natural Science Fund of China (Project No. 71871090), and Hunan Provincial Science & Technology Innovation Leading Project (2020GK2005).

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Authors and Affiliations

  1. Hunan University, Changsha, 410082, China

    Baobao Song & Qiujun Lan

  2. The Australian National University, Canberra, ACT, 2600, Australia

    Yang Li

  3. Deakin University, Geelong, VIC, 3216, Australia

    Gang Li

Authors
  1. Baobao Song
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  2. Qiujun Lan
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  3. Yang Li
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  4. Gang Li
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Corresponding author

Correspondence to Gang Li .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Song, B., Lan, Q., Li, Y., Li, G. (2022). Distributed Differentially Private Ranking Aggregation. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-05933-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05932-2

  • Online ISBN: 978-3-031-05933-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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