Publishing Graph Data with Subgraph Differential Privacy
- Title
- Publishing Graph Data with Subgraph Differential Privacy
- Authors
- Nguyen, Phuong Binh
- Date Issued
- 2015
- Publisher
- 포항공과대학교
- Abstract
- The eruption of social networks, communication networks etc. makes them become valuable resources for the research community. However, the graph data owners hesitate to share their data due to the barrier of privacy leakage. In this work, we propose a new privacy definition, called subgraph-differential privacy (subgraph-DP), for graph data publishing based on the conventional differential privacy definition. Subgraph-DP is against the graph-based attacks by restricting the adversaries predict the true subgraph with a high confidence. We provide the mechanism that gives subgraph-DP in which noise will be added to a small set of edges to make sure that all k-vertices connected subgraphs are perturbed. The experimental results show that our perturbation mechanism preserves most of the important statistic features of graph while
still guarantees privacy. It is flexible that the owners can adapt the mechanism to decide what they want to publish. We also discuss some limitations in our work.
- URI
- http://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002066365
https://oasis.postech.ac.kr/handle/2014.oak/93388
- Article Type
- Thesis
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.