Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-17143-7_40
Reference18 articles.
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
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
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