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PRIVIC: A privacy-preserving method for incremental collection of location data.

, and . Proc. Priv. Enhancing Technol., 2024 (1): 582-596 (January 2024)

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PRIVIC: A privacy-preserving method for incremental collection of location data., and . Proc. Priv. Enhancing Technol., 2024 (1): 582-596 (January 2024)Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response., , and . CoRR, (2022)Stock Price Prediction using Bidirectional LSTM with Attention.. ICAIC, page 1-5. IEEE, (2022)Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response., , and . FPS (1), volume 14551 of Lecture Notes in Computer Science, page 440-458. Springer, (2023)Understanding and optimizing the trade-off between privacy and utility from a foundational perspective. (Understanding and optimizing the trade-off between privacy and utility from a foundational perspective).. École Polytechnique, Palaiseau, France, (2023)Group privacy for personalized federated learning., , , , and . CoRR, (2022)Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond., , , , and . CoRR, (2023)Tight Differential Privacy Blanket for Shuffle Model., , and . CoRR, (2022)Group Privacy for Personalized Federated Learning., , , , and . ICISSP, page 252-263. SciTePress, (2023)Establishing the Price of Privacy in Federated Data Trading., , and . Protocols, Strands, and Logic, volume 13066 of Lecture Notes in Computer Science, page 232-250. Springer, (2021)