Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sharing Social Network Data: Differentially Private Estimation of
  Exponential-Family Random Graph Models   Motivated by a real-life problem of sharing social network data that contain
sensitive personal information, we propose a novel approach to release and
analyze synthetic graphs in order to protect privacy of individual
relationships captured by the social network while maintaining the validity of
statistical results. A case study using a version of the Enron e-mail corpus
dataset demonstrates the application and usefulness of the proposed techniques
in solving the challenging problem of maintaining privacy \emph{and} supporting
open access to network data to ensure reproducibility of existing studies and
discovering new scientific insights that can be obtained by analyzing such
data. We use a simple yet effective randomized response mechanism to generate
synthetic networks under $\epsilon$-edge differential privacy, and then use
likelihood based inference for missing data and Markov chain Monte Carlo
techniques to fit exponential-family random graph models to the generated
synthetic networks.

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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph