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Collecting and analyzing key-value data under shuffled differential privacy

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References

  1. Balle B, Bell J, Gascón A, Nissim K. The privacy blanket of the shuffle model. In: Proceedings of the 39th Annual International Cryptology Conference on Advances in Cryptology. 2019, 638–667

  2. Gu X, Li M, Cheng Y, Xiong L, Cao Y. PCKV: locally differentially private correlated key-value data collection with optimized utility. In: Proceedings of the 29th USENIX Conference on Security Symposium. 2020, 55

  3. Bittau A, Erlingsson Ú, Maniatis P, Mironov I, Raghunathan A, Lie D, Rudominer M, Kode U, Tinnes J, Seefeld B. Prochlo: strong privacy for analytics in the crowd. In: Proceedings of the 26th Symposium on Operating Systems Principles. 2017, 441–459

  4. Wang S, Li J, Qian Y, Du J, Lin W, Yang W. Hiding numerical vectors in local private and shuffled messages. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2021, 3706–3712

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61902365 and 61902366), and Open Project Program from Key Lab of Cryptologic Technology and Information Security, Ministry of Education, Shandong University.

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Authors and Affiliations

  1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Ning Wang, Wei Zheng, Zhigang Wang & Zhiqiang Wei

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Yu Gu & Ge Yu

  3. Key Lab of Cryptologic Technology and Information Security, Ministry of Education, Shandong University, Qingdao, 266200, China

    Peng Tang

  4. School of Cyber Science and Technology, Shandong University, Qingdao, 266200, China

    Peng Tang

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  1. Ning Wang
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  2. Wei Zheng
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  3. Zhigang Wang
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  4. Zhiqiang Wei
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  5. Yu Gu
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  6. Peng Tang
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  7. Ge Yu
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Corresponding author

Correspondence to Peng Tang.

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Wang, N., Zheng, W., Wang, Z. et al. Collecting and analyzing key-value data under shuffled differential privacy. Front. Comput. Sci. 17, 172606 (2023). https://doi.org/10.1007/s11704-022-1572-0

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