2016 Symposium Posters

Posters > 2016

Local Differential Privacy Preserving in Social Networks


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Primary Investigator:
Feng Li

Project Members
Tianchong Gao; Feng Li
Abstract
Online social networks often contain sensitive information about individuals. Therefore, de-anonymizing the graph before releasing becomes an important issue. We define the notion of group based local differential privacy. In particular, by resolving the network into 1-neighborhood graphs and applying HRG-based methods, local differential privacy reduces the noise scale. By grouping the HRGs’ output spaces with overlap together and sampling an outstanding HRG, we perturbed the subgraphs in a group to be extremely similar to enhance the privacy power beyond differential privacy..

Our annual information security symposium will take place on April 3rd and 4th, 2018.
Purdue University, West Lafayette, IN