Data Stream Aggregation Mechanism Under Local Differential Privacy
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1332-5_17
Reference20 articles.
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2. Xue, Q., Ye, Q., Hu, H., Zhu, Y., Wang, J.: DDRM: a continual frequency estimation mechanism with local differential privacy. In: IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 6784–6797 (2023). https://doi.org/10.1109/TKDE.2022.3177721
3. Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? SIAM J. Comput. 40(3), 793–826 (2011)
4. 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 (2014)
5. Dong, W., Luo, Q., Yi, K.: Continual Observation under User-level Differential Privacy. In: 2023 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, pp. 2190–2207 (2023).https://doi.org/10.1109/SP46215.2023.10179466
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