(ϵ, δ)-local differential privacy mechanisms for set-valued data analysis

Bing CHEN , Youwen ZHU

Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (6) : 2006615

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Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (6) : 2006615 DOI: 10.1007/s11704-025-50469-y
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(ϵ, δ)-local differential privacy mechanisms for set-valued data analysis

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Bing CHEN, Youwen ZHU. (ϵ, δ)-local differential privacy mechanisms for set-valued data analysis. Front. Comput. Sci., 2026, 20(6): 2006615 DOI:10.1007/s11704-025-50469-y

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References

[1]

Duchi J C, Jordan M I, Wainwright M J. Local privacy and statistical minimax rates. In: Proceedings of the 54th IEEE Annual Symposium on Foundations of Computer Science. 2013, 429−438

[2]

Bassily R, Smith A. Local, private, efficient protocols for succinct histograms. In: Proceedings of the 47th Annual ACM Symposium on Theory of Computing. 2015, 127−135

[3]

Wang T, Zhao J, Hu Z, Yang X, Ren X, Lam K Y . Local Differential Privacy for data collection and analysis. Neurocomputing, 2021, 426: 114–133

[4]

Zhang Y, Zhu Y, Wang S, Huang X . Mean estimation of numerical data under (ϵ,δ)-utility-optimized local differential privacy. IEEE Transactions on Information Forensics and Security, 2024, 19: 9656–9669

[5]

Wang S, Huang L, Nie Y, Wang P, Xu H, Yang W. PrivSet: set-valued data analyses with locale differential privacy. In: Proceedings of the IEEE Conference on Computer Communications. 2018, 1088−1096

[6]

Wang S, Qian Y, Du J, Yang W, Huang L, Xu H . Set-valued data publication with local privacy: tight error bounds and efficient mechanisms. Proceedings of the VLDB Endowment, 2020, 13( 8): 1234–1247

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