Skip to main content

Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python

  • Conference paper
  • First Online:
Computer Security – ESORICS 2022 (ESORICS 2022)

Abstract

This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-world implementations by big tech companies such as Google, Apple, and Microsoft. The primary application of LDP is frequency (or histogram) estimation, in which the aggregator estimates the number of times each value has been reported. The presented package provides an easy-to-use and fast implementation of state-of-the-art solutions and LDP protocols for frequency estimation of: single attribute (i.e., the building blocks), multiple attributes (i.e., multidimensional data), multiple collections (i.e., longitudinal data), and both multiple attributes/collections. Multi-freq-ldpy is built on the well-established Numpy package – a de facto standard for scientific computing in Python – and the Numba package for fast execution. These features are described and illustrated in this paper with two worked examples. This package is open-source and publicly available under an MIT license via GitHub (https://github.com/hharcolezi/multi-freq-ldpy) and can be installed via PyPi (https://pypi.org/project/multi-freq-ldpy/).

Authors are listed by order of contribution. See [3] for the full version of this paper

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://pypi.org/project/multi-freq-ldpy/.

  2. 2.

    A more complete Python package for single frequency estimation can be found in (https://pypi.org/project/pure-ldp/) [5].

  3. 3.

    Originally known as basic one-time RAPPOR [9].

  4. 4.

    Naturally, \(0< \epsilon _1 \ll \epsilon _{perm}\) because higher values of \(\epsilon _1\) are undesirable [2, 9].

References

  1. Arcolezi, H.H., Couchot, J.F., Al Bouna, B., Xiao, X.: Random sampling plus fake data: Multidimensional frequency estimates with local differential privacy. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 47–57 (2021). https://doi.org/10.1145/3459637.3482467

  2. Arcolezi, H.H., Couchot, J.F., Bouna, B.A., Xiao, X.: Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates. Digit. Commun. Netw. (2022). https://doi.org/10.1016/j.dcan.2022.07.003

    Article  Google Scholar 

  3. Arcolezi, H.H., Couchot, J.F., Gambs, S., Palamidessi, C., Zolfaghari, M.: Multi-Freq-LDPy: multiple frequency estimation under local differential privacy in python. arXiv preprint arXiv:2205.02648 (2022)

  4. Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing. ACM (2015). https://doi.org/10.1145/2746539.2746632

  5. Cormode, G., Maddock, S., Maple, C.: Frequency estimation under local differential privacy. Proceed. VLDB Endowment 14(11), 2046–2058 (2021). https://doi.org/10.14778/3476249.3476261

  6. Ding, B., Kulkarni, J., Yekhanin, S.: Collecting telemetry data privately. In: Guyon, I., et al.(eds.) Advances in Neural Information Processing Systems 30, pp. 3571–3580. Curran Associates, Inc. (2017)

    Google Scholar 

  7. Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science. IEEE (2013). https://doi.org/10.1109/focs.2013.53

  8. 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 

  9. Erlingsson, U., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054–1067. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2660267.2660348

  10. Kairouz, P., Bonawitz, K., Ramage, D.: Discrete distribution estimation under local privacy. In: Conference on Machine Learning, pp. 2436–2444. PMLR (2016)

    Google Scholar 

  11. Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? In: 2008 49th Annual IEEE Symposium on Foundations of Computer Science. IEEE (2008). https://doi.org/10.1109/focs.2008.27

  12. Lam, S.K., Pitrou, A., Seibert, S.: Numba: A LLVM-based python JIT compiler. In: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC. LLVM 2015, Association for Computing Machinery, New York, NY, USA (2015). https://doi.org/10.1145/2833157.2833162

  13. 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. arXiv preprint arXiv:1606.05053 (2016)

  14. van der Walt, S., Colbert, S.C., Varoquaux, G.: The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22–30 (2011). https://doi.org/10.1109/MCSE.2011.37

    Article  Google Scholar 

  15. Wang, N., et al.: Collecting and analyzing multidimensional data with local differential privacy. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE (2019). https://doi.org/10.1109/icde.2019.00063

  16. Wang, S., et al.: Mutual information optimally local private discrete distribution estimation. arXiv preprint arXiv:1607.08025 (2016)

  17. Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: 26th USENIX Security Symposium (USENIX Security 17), pp. 729–745. USENIX Association, Vancouver, BC (2017)

    Google Scholar 

  18. Ye, M., Barg, A.: Optimal schemes for discrete distribution estimation under locally differential privacy. IEEE Trans. Inf. Theory 64(8), 5662–5676 (2018). https://doi.org/10.1109/TIT.2018.2809790

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the European Research Council (ERC) project HYPATIA under the European Union’s Horizon 2020 research and innovation programme. Grant agreement n. 835294. The work of Jean-François Couchot was supported by the EIPHI-BFC Graduate School (contract “ANR-17-EURE-0002"). Sébastien Gambs is supported by the Canada Research Chair program as well as a Discovery Grant from NSERC.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Héber H. Arcolezi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Arcolezi, H.H., Couchot, JF., Gambs, S., Palamidessi, C., Zolfaghari, M. (2022). Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python. In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds) Computer Security – ESORICS 2022. ESORICS 2022. Lecture Notes in Computer Science, vol 13556. Springer, Cham. https://doi.org/10.1007/978-3-031-17143-7_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17143-7_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17142-0

  • Online ISBN: 978-3-031-17143-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics