FUZZ-IEEE - On some clustering approaches for graphs
Creators
- 1. Universitat Rovira i Virgili, Dept. of Computer Engineering and Maths, UNESCO Chair in Data Privacym Av. P aïsos Catalans 26, 43007 Tarragona, Catalonia, Spain
- 2. Autonomous University of Barcelona
- 3. Spanish National Research Council
Description
In this paper we discuss some tools for graph perturbation with applications to data privacy. We present and analyse two different approaches. One is based on matrix decomposition and the other on graph partitioning. We discuss these methods and show that they belong to two traditions in data protection: noise addition/microaggregation and k-anonymity.
References