k-degree anonymity

Install requirements

pip install -r requirements.txt

Usage

usage: python k-degree.py k_value graph_to_anonymize.csv
usage: python print_metrics.py array_of_norm cc_supergraph apl_supergraph cc_original_graph apl_original_graph

Example

usage: python k-degree.py 3 Dataset/graph_friend_100_10_100.csv
usage: print_metrics ./Metrics/array_norm.csv ./Metrics/cc_supergraph.csv ./Metrics/avg_path_1000.csv 0.07286909946469872 2.0787367367367366

Dataset

In Dataset directory you can find 4 datasets:

• Dataset\graph_friend_6.csv
• Dataset\graph_friend_1000_10_100.csv
• Dataset\graph_friend_10000_100_1000.csv

Metrics

In Metrics directory you can find 3 files:

• Metrics/array_norm.csv
• Metrics/cc_supergraph.csv
• Metrics/avg_path_1000.csv

K for supergraph graph_friend_10000_100_1000.csv

3, 5, 6, 12, 13, 14, 16, 20, 24, 30, 33, 34, 38, 40, 48, 50

K for supergraph graph_friend_1000_10_100.csv

7, 9, 10, 12, 15, 16, 17, 20, 22

Where the first number is the number of nodes, instead, the second and the third number is the min and max link for each node.

Each line inside .csv file is an adjacency list.