**r2d3**

The r2d3 package provides a suite of tools for using D3 visualizations with R, including:

• Translating R objects into D3 friendly data structures

• Rendering D3 scripts within the RStudio Viewer and R Notebooks

• Publishing D3 visualizations to the web

• Incorporating D3 scripts into R Markdown reports, presentations, and dashboards

• Creating interactive D3 applications with Shiny

• Distributing D3 based htmlwidgets in R packages … **Regression-via-Classification**

Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. … **Local Differential Privacy**

Real-time data-driven optimization and control problems over networks may require sensitive information of participating users to calculate solutions and decision variables, such as in traffic or energy systems. Adversaries with access to coordination signals may potentially decode information on individual users and put user privacy at risk. We develop \emph{local differential privacy}, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have, for a larger family of convex distributed optimization problems. The mechanism allows agent to customize their own privacy level based on local needs and parameter sensitivities. We propose a general sampling based approach for determining sensitivity and derive analytical bounds for specific quadratic problems. We analyze inherent trade-offs between privacy and suboptimality and propose allocation schemes to divide the maximum allowable noise, a \emph{privacy budget}, among all participating agents. Our algorithm is implemented to enable privacy in distributed optimal power flow for electric grids. … **Individually-Private Information Retrieval with Side Information (IPIR-SI)**

We consider a multi-user variant of the private information retrieval problem described as follows. Suppose there are $D$ users, each of which wants to privately retrieve a distinct message from a server with the help of a trusted agent. We assume that the agent has a random subset of $M$ messages that is not known to the server. The goal of the agent is to collectively retrieve the users’ requests from the server. For protecting the privacy of users, we introduce the notion of individual-privacy — the agent is required to protect the privacy only for each individual user (but may leak some correlations among user requests). We refer to this problem as Individually-Private Information Retrieval with Side Information (IPIR-SI). We first establish a lower bound on the capacity, which is defined as the maximum achievable download rate, of the IPIR-SI problem by presenting a novel achievability protocol. Next, we characterize the capacity of IPIR-SI problem for $M = 1$ and $D = 2$. In the process of characterizing the capacity for arbitrary $M$ and $D$ we present a novel combinatorial conjecture, that may be of independent interest. …

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Aug 2022

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