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\title{Differentially Private Multi-Party Data Release for Linear Regression}

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\author[1]{\href{mailto:rw565@cornell.edu}{Ruihan Wu$^{*}$}}
\author[2]{Xin Yang}
\author[2]{Yuanshun Yao}
\author[2]{Jiankai Sun}
\author[2]{Tianyi Liu}
\author[1]{Kilian Q. Weinberger}
\author[2]{Chong Wang}
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    Cornell University, USA
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    ByteDance Inc., USA
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\affil[*]{%
    Work done as an intern at ByteDance Inc.
}
  
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\begin{abstract}
\emph{Differentially Private (DP) data release} is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focused on scenarios where a single party owns all the data.
In this paper we focus on the multi-party setting, where different stakeholders own disjoint sets of attributes belonging to the same group of data subjects.
Within the context of linear regression that allow all parties to train models on the complete data without the ability to infer private attributes or identities of individuals, we start with directly applying Gaussian mechanism and show it has the small eigenvalue problem.
We further propose our novel method and prove it asymptotically converges to the optimal (non-private) solutions with increasing dataset size.
We substantiate the theoretical results through experiments on both artificial and real-world datasets.  
\end{abstract}

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\begin{acknowledgements}
The authors thank Xiaowei Zhang for drawing pictures in Figure \ref{fig:motivating_example}. RW and KQW are supported by grants from the National Science Foundation NSF (IIS-2107161, III-1526012, IIS-1149882, and IIS-1724282), and the Cornell Center for Materials Research with funding from the NSF MRSEC program (DMR-1719875), and SAP America.
\end{acknowledgements}

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