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We investigate the local differential privacy (LDP) guarantees of a randomized privacy mechanism via its contraction properties.
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L. P. Barnes, W. N. Chen, and A. Özgür, “Fisher information under local differential privacy,”
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J. Acharya, C. L. Canonne, and H. Tyagi, “Inference under information constraints i: Lower bounds from chi-square contraction,”
2020
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D. Wang and J. Xu, “On sparse linear regression in the local differential privacy model,”
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2020
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S. Asoodeh, M. Diaz, and F. P. Calmon, “Privacy analysis of online learning algorithms via contraction coefficients,”
2020
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A. Makur and L. Zheng, “Comparison of contraction coefficients for
2020
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S. Asoodeh, J. Liao, F. P. Calmon, O. Kosut, and L. Sankar, “Three variants of differential privacy: Lossless conversion and applications,”
2021
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