def main():
csvfile = open(result_file, 'a')
result_writer = csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
#net_name = network_name[int(sys.argv[1])]
for net_name in network_name:
print("Net:", net_name)
net = nx.read_edgelist(network_path + net_name + ".txt",
create_using=nx.Graph(),
nodetype=int)
origin_edge_count = net.number_of_edges()
for epsilon in [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]:
delta = epsilon
result_writer.writerow([time.time(),
"edge_privacy",
"origin",
net_name,
origin_edge_count,
origin_edge_count,
origin_edge_count,
epsilon,
delta])
for _ in range(int(sys.argv[1])):
edge_sampled_net = basic_edge.private_basic_edge_sample(net,
epsilon,
delta)
result_writer.writerow([time.time(),
"edge_privacy",
"basic_edge",
net_name,
edge_sampled_net.number_of_edges(),
edge_sampled_net.number_of_edges() / min(1 - math.exp(-epsilon), delta, 1.0),
origin_edge_count,
epsilon,
delta])
color_sampled_net = color.private_color_sample(net,
epsilon,
delta)
result_writer.writerow([time.time(),
"edge_privacy",
"color",
net_name,
color_sampled_net.number_of_edges(),
color_sampled_net.number_of_edges() / min(1.0, delta),
origin_edge_count,
epsilon,
delta])
csvfile.flush()
def main():
csvfile = open(result_file, 'a')
result_writer = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
#net_name = network_name[int(sys.argv[1])]
for net_name in network_name:
print("Net:", net_name)
net = nx.read_edgelist(network_path + net_name + ".txt",
create_using=nx.Graph(),
nodetype=int)
for epsilon in [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]:
delta = epsilon
original_dist = compute_degree_distribution(net, limit=LIMIT)
for i in range(LIMIT + 1):
result_writer.writerow([
time.time(), "edge_privacy", "origin", net_name, i,
original_dist[i], epsilon, delta
])
edge_sampled_dists = [None] * int(sys.argv[1])
color_sampled_dists = [None] * int(sys.argv[1])
for i in range(int(sys.argv[1])):
edge_sampled_dists[i] = compute_degree_distribution(
basic_edge.private_basic_edge_sample(net, epsilon, delta),
limit=LIMIT)
color_sampled_dists[i] = compute_degree_distribution(
color.private_color_sample(net, epsilon, delta),
limit=LIMIT)
average_edge_dist = np.mean(
np.array(edge_sampled_dists),
axis=0) # / min(1 - math.exp(-epsilon), delta, 1.0)
average_color_dist = np.mean(np.array(color_sampled_dists),
axis=0) # / min(1.0, delta)
for i in range(LIMIT + 1):
result_writer.writerow([
time.time(), "edge_privacy", "basic_edge", net_name, i,
average_edge_dist[i], epsilon, delta
])
result_writer.writerow([
time.time(), "edge_privacy", "color", net_name, i,
average_color_dist[i], epsilon, delta
])
csvfile.flush()
def main():
csvfile = open(result_file, 'a')
result_writer = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
true_k_core = {}
for net_name in network_name:
net = nx.read_edgelist(network_path + net_name + ".txt",
create_using=nx.Graph(),
nodetype=int)
net.remove_edges_from(nx.selfloop_edges(net))
for k in [512, 256, 128, 64, 32, 16, 8, 4]:
true_k_core[(net_name, k)] = nx_core.k_core(net, k)
# result_writer.writerow([time.time(),
# "node_privacy",
# "true",
# "k_core",
# net_name,
# 0.5,
# 0.5,
# k, # reserve for index
# true_k_core
# ])
for i in range(int(sys.argv[1])):
print("Repeat:", i)
for net_name in network_name:
print("Net:", net_name)
net = nx.read_edgelist(network_path + net_name + ".txt",
create_using=nx.Graph(),
nodetype=int)
net.remove_edges_from(nx.selfloop_edges(net))
for k in [512, 256, 128, 64, 32, 16, 8, 4]:
for epsilon in [0.5, 0.1, 0.05]:
delta = epsilon
basic_node_k_core = nx_core.k_core(
basic_node.private_basic_node_sample(
net, epsilon, delta),
k * min(1 - math.exp(-epsilon), delta, 1.0)**2)
result_writer.writerow([
time.time(),
"node_privacy",
"basic_node",
"k_core",
net_name,
epsilon,
delta,
k, #reserve for index
jaccard(true_k_core[(net_name, k)], basic_node_k_core)
])
basic_edge_k_core = nx_core.k_core(
basic_edge.private_basic_edge_sample(
net, epsilon, delta),
k * min(1 - math.exp(-epsilon), delta, 1.0))
result_writer.writerow([
time.time(), "edge_privacy", "basic_edge", "k_core",
net_name, epsilon, delta, k,
jaccard(true_k_core[(net_name, k)], basic_edge_k_core)
])
color_k_core = nx_core.k_core(
color.private_color_sample(net, epsilon, delta),
k * min(1.0, delta))
result_writer.writerow([
time.time(), "edge_privacy", "color", "k_core",
net_name, epsilon, delta, k,
jaccard(true_k_core[(net_name, k)], color_k_core)
])
csvfile.flush()
blocki_edge_100_k_core = nx_core.k_core(
blocki_edge.blocki_edge_trim(net, 100), k)
for epsilon in [0.5, 0.1, 0.05]:
result_writer.writerow([
time.time(), "edge_privacy", "blocki_edge_100",
"k_core", net_name, epsilon, epsilon, k,
jaccard(true_k_core[(net_name, k)],
blocki_edge_100_k_core)
])
blocki_edge_1000_k_core = nx_core.k_core(
blocki_edge.blocki_edge_trim(net, 1000), k)
for epsilon in [0.5, 0.1, 0.05]:
result_writer.writerow([
time.time(), "edge_privacy", "blocki_edge_1000",
"k_core", net_name, epsilon, epsilon, k,
jaccard(true_k_core[(net_name, k)],
blocki_edge_1000_k_core)
])
ding_26_k_core = nx_core.k_core(ding.ding_trim(net, 100), k)
for epsilon in [0.5, 0.1, 0.05]:
result_writer.writerow([
time.time(), "edge_privacy", "ding_100", "k_core",
net_name, epsilon, epsilon, k,
jaccard(true_k_core[(net_name, k)], ding_26_k_core)
])
csvfile.flush()