<ul><li>The paper proposes a method for recovering latent information from graphs under local differential privacy.</li><li>The authors show that a standard local differential privacy mechanism induces a specific geometric distortion in the latent positions of generalized random dot-product graphs.</li><li>They demonstrate that consistent recovery of the latent positions can be achieved by adjusting the statistical inference procedure for the privatized graph.</li><li>The proposed procedure is nearly minimax-optimal under local edge differential privacy constraints and allows for consistent recovery of geometric and topological information.</li></ul>

Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacy

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