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\title{Privacy-Aware Compression for Federated Data Analysis}

% Or {Privacy-Aware Compression for Federated Analytics/Statistics}
% Or {Principled Privacy-aware compression for Federated Statistics}

% The standard author block has changed for UAI 2022 to provide
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\author{%
  \name Kamalika Chaudhuri$^{*1}$ \email{kamalika@fb.com}\\
  \name Chuan Guo$^{*1}$ \email{chuanguo@fb.com}\\
  \name Mike Rabbat$^{1}$ \email{mikerabbat@fb.com}\\
  \addr{$^{1}$Facebook AI Research}\\
  \addr{$^{*}$Equal contribution}
}

\begin{document}
\maketitle

%\begin{abstract}
%Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-power user devices to estimate aggregate statistics. Two major challenges in this learning paradigm are privacy, since user data is often sensitive, and compression since the devices involved are small and low-power. Prior work has addressed this challenge by applying standard compression algorithm on top of known privacy mechanisms.
%	In this work, we take a more principled look at the problem, and design privacy-aware compression mechanisms that work for any given communication budget. We propose a mechanism  for transmitting a single real number that is optimal under certain conditions, and show how to extend it to metric differential privacy for location privacy use-cases, as well as vectors, for applications to federated learning. Our experiments illustrate that our mechanism can lead to better utility vs. compression trade-offs for the same privacy loss in both use-cases. 
%\end{abstract}

%\kc{alternative abstract keeping in mind mechanisms}

\begin{abstract}
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate aggregate statistics. Two major challenges in this framework are privacy, since user data is often sensitive, and compression, since the user devices have low network bandwidth. Prior work has addressed these challenges separately by combining standard compression algorithms with known privacy mechanisms.
In this work, we take a holistic look at the problem and design a family of privacy-aware compression mechanisms that work for any given communication budget. We first propose a mechanism for transmitting a single real number that has optimal variance under certain conditions. We then show how to extend it to metric differential privacy for location privacy use-cases, as well as vectors, for application to federated learning. Our experiments illustrate that our mechanism can lead to better utility vs. compression trade-offs for the same privacy loss in a number of settings. \end{abstract}



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\section*{Acknowledgements}
We thank Graham Cormode and Huanyu Zhang for insightful comments and suggestions that helped shape our final draft.

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