Privacy-Preserving Approach PBCN in Social Network With Differential Privacy: For a given Graphical Dataset, We Create a new dataset following a certain Algorithm and then compare Differential Privacy and Privacy protection level(P) Output P value(The protection level) Then plot for the running time vs No.of edges Protection level vs privacy budget Installation Guide pip! install random
pip! install networkx
pip! install copy Note For User-Desired input,
original_graph = generate_random_graph(num_edges)
What i am doing above is creating my own data.Since the code mentioned in Research paper is too huge to work on my device.But lets say you want Graph_user_Input to be used just do
original_graph = Graph_user_Input Dependencies: numpy matplot networkx Copy Random Resources: RESEARCH PAPER: https://ieeexplore.ieee.org/iel7/4275028/9114348/09044400.pdf DATASET:https://snap.stanford.edu/data/wiki-Vote.html