Abstract
The release of differentially private streaming data has been extensively studied, yet striking a good balance between privacy and utility on temporally correlated data in the stream remains an open problem. Existing works focus on enhancing privacy when applying differential privacy to correlated data, highlighting that differential privacy may suffer from additional privacy leakage under correlations; consequently, a small privacy budget has to be used which worsens the utility. In this work, we propose a post-processing framework to improve the utility of differential privacy data release under temporal correlations. We model the problem as a maximum posterior estimation given the released differentially private data and correlation model and transform it into nonlinear constrained programming. Our experiments on synthetic datasets show that the proposed approach significantly improves the utility and accuracy of differentially private data by nearly a hundred times in terms of mean square error when a strict privacy budget is given.
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Notes
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Please note that our method can be applied to other mechanisms. However, for the duration of this article, we have temporarily chosen to default to the Laplace Mechanism.
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Acknowledgments
This work was partially supported by JST CREST JPMJCR21M2, JST SICORP JPMJSC2107, JSPS KAKENHI Grant Numbers 19H04215, 21K19767, 22H03595 and 22H00521. Additionally, Xuyang would like to express appreciation to tutor for his meticulous instruction, as well as to his family and friend Yuchan Z. for their encouraging support in his research endeavors.
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Cao, X., Cao, Y., Pappachan, P., Nakamura, A., Yoshikawa, M. (2023). Differentially Private Streaming Data Release Under Temporal Correlations via Post-processing. In: Atluri, V., Ferrara, A.L. (eds) Data and Applications Security and Privacy XXXVII. DBSec 2023. Lecture Notes in Computer Science, vol 13942. Springer, Cham. https://doi.org/10.1007/978-3-031-37586-6_12
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DOI: https://doi.org/10.1007/978-3-031-37586-6_12
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