Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy

arXiv:2405.02341v1 Announce Type: cross
Abstract: We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L_2$ geometry, resulting in suboptimal leading constants in mean square errors (MSEs); secondly, schemes achieving order-optimal communication-privacy trade-offs do not extend seamlessly to streaming differential privacy (DP) settings (e.g., tree aggregation …

abstract adapt arxiv challenges communication constraints cs.cr cs.lg differential differential privacy geometry key mean privacy random representation rotation streaming study trade type