Differential Privacy from Locally Adjustable Graph Algorithms: k-Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs

@article{Dhulipala2022DifferentialPF,
  title={Differential Privacy from Locally Adjustable Graph Algorithms: k-Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs},
  author={Laxman Dhulipala and Quanquan C. Liu and Sofya Raskhodnikova and Jessica Shi and Julian Shun and Shangdi Yu},
  journal={2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)},
  year={2022},
  pages={754-765}
}
Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between individuals. While there exists a rich history of important literature in this space, to the best of our knowledge, no results formalize a relationship between certain parallel and distributed graph algorithms and differentially private graph analysis… 

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