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'''
Created on May 10, 2010
@author: jose
'''
from cloudlight.algorithms.privacy import LinkPrivacyModel
import random
class PrivacyAttackStrategiesException(Exception):
'''
classdocs
'''
pass
class PrivacyAttackStrategies(object):
'''
Different attack strategies to target LinkPrivacyModels
'''
def __init__(self, graph, lookahead, coverage_funcs=['node'], debug=True):
'''
graph : graph where the attack take place
looakead : integer measuring the visibility of the nodes
coverage_func : choose "node" for default node_coverage or "link" for link_coverage of the attack
'''
self.model = LinkPrivacyModel(graph, lookahead, debug)
self.debug = debug
self.node_rand_seed = None
self.coverage_types = coverage_funcs
self.model.coverage_types = coverage_funcs
self.coverage_functions = []
if 'node' in coverage_funcs :
self.coverage_functions.append( (self.model.node_coverage, 'node_coverage') )
if 'link' in coverage_funcs:
self.coverage_functions.append( (self.model.link_coverage, 'link_coverage') )
if 'triangle' in coverage_funcs:
self.coverage_functions.append( (self.model.triangle_coverage, 'triangle_coverage') )
if 'complete_node' in coverage_funcs or 'korolova' in coverage_funcs :
self.coverage_functions.append( (self.model.korolova_node_coverage, 'complete_node_coverage') )
def start_node_order(self, coverages={'node':[0.05,0.10],'link':[0.05,0.10]}, max_effort=None, nlist=[], name=None):
if len(coverages) < 1:
raise PrivacyAttackStrategiesException('Error: strategies need a non-empty listo of graph float coverages!')
for node in nlist:
if self.debug:
print 'bribing node... %s -> %s' % (str(name),node)
print 'lookahead %d' % self.model.lookahead
self.model.add_bribed_node(node)
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
yield self.model.total_effort(), cov, cov_func_name
if self.model.total_effort() >= max_effort:
break
# finish signal!
yield -1, None, None
def start_degree(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_degree'
for effort, coverage_frac, coverage_type in self.start_node_order(coverages, max_effort, self.model.sorted_degrees_dec_iter(), 'start_degree'):
yield effort, coverage_frac, coverage_type
def start_triangles(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_triangles'
for effort, coverage_frac, coverage_type in self.start_node_order(coverages, max_effort, self.model.sorted_triangles_dec_iter(), 'start_triangles'):
yield effort, coverage_frac, coverage_type
def start_random(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_random'
for effort, coverage_frac, coverage_type in self.start_node_order(coverages, max_effort, self.model.random_node_order_iter(), 'start_random'):
yield effort, coverage_frac, coverage_type
def start_next_node(self, coverages=[0.20], max_effort=None, next_func=None):
prev_node = None
if len(coverages) < 1:
raise PrivacyAttackStrategiesException('Error: strategies need a non-empty listo of graph float coverages!')
nc = 0
while nc < self.model.graph.number_of_nodes():
node = next_func()
if node == None or (prev_node and node == prev_node):
break
else:
prev_node = node
if self.debug:
print 'bribing node... %s -> %s' % (str(next_func),node)
print 'lookahead %d' % self.model.lookahead
self.model.add_bribed_node(node)
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
yield self.model.total_effort(), cov, cov_func_name
if self.model.total_effort() >= max_effort:
break
nc += 1
# finish signal!
yield -1, None, None
def start_greedy(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_greedy'
return self.start_next_node(coverages, max_effort, self.model.max_unseen_degree_node)
def start_greedy_triangles(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_greedy_triangles'
return self.start_next_node(coverages, max_effort, self.model.max_unseen_triangles_node)
def start_greedy_seen_degree(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_greedy_seen_degree'
return self.start_next_node(coverages, max_effort, self.model.max_seen_degree_node)
def start_greedy_seen_triangles(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_greedy_seen_triangles'
return self.start_next_node(coverages, max_effort, self.model.max_seen_triangles_node)
def start_crawler(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler'
return ( self.start_crawler_generic(coverages, max_effort, self.model.max_unseen_degree_crawler_node) )
def start_crawler_triangles(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_triangles'
return ( self.start_crawler_generic(coverages, max_effort, self.model.max_unseen_triangles_crawler_node) )
def start_crawler_seen_degree(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_seen_degree'
return ( self.start_crawler_generic(coverages, max_effort, self.model.max_seen_degree_crawler_node) )
def start_crawler_random(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_random'
return ( self.start_crawler_generic(coverages, max_effort, self.model.random_crawler_node) )
def start_crawler_seen_triangles(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_seen_triangles'
return ( self.start_crawler_generic(coverages, max_effort, self.model.max_seen_triangles_crawler_node) )
def start_crawler_degree_hist(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_degree_hist'
self.model.initialize_histogram_degree()
next_node = self.model.histogram_degree_crawler_node
return ( self.start_crawler_generic(coverages, max_effort, next_node) )
def start_crawler_degree_hist_bin_dist(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_degree_hist_bin_dist'
self.model.initialize_histogram_degree()
next_node = self.model.histogram_degree_crawler_node_bin_dist
return ( self.start_crawler_generic(coverages, max_effort, next_node) )
def start_crawler_degree_aprox_hist_bin_dist(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_degree_aprox_hist_bin_dist'
deg_dist = lambda deg : 0.844 * deg**-1.844 # TODO
self.model.initialize_histogram_degree_dist(degree_dist=deg_dist, number_of_nodes=self.model.number_of_nodes)
next_node = self.model.histogram_degree_crawler_node_bin_dist
return ( self.start_crawler_generic(coverages, max_effort, next_node) )
def start_crawler_degree_hist_bin_dist_orderby_triangles(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_degree_hist_bin_dist_orderby_triangles'
self.model.initialize_histogram_degree()
next_node = self.model.histogram_degree_crawler_node_bin_dist_orderby_triangles
return ( self.start_crawler_generic(coverages, max_effort, next_node) )
def start_crawler_degree_hist_bin_dist_rand(self, coverages=[0.20], max_effort=999999999):
self.model.strategy = 'start_crawler_degree_hist_bin_dist_rand'
self.model.initialize_histogram_degree()
next_node = self.model.histogram_degree_crawler_node_bin_dist_rand
return ( self.start_crawler_generic(coverages, max_effort, next_node) )
def start_crawler_generic(self, coverages=[0.20], max_effort=999999999, next_func=None):
# use the same seed of choose random node to start crawling...
if self.node_rand_seed:
node = self.node_rand_seed
else:
raise Exception('NOOOOOOOOOO')
node = None
for n in self.model.random_node_order_iter():
node = n
break
#node = self.model.random_node()
if self.debug:
print 'bribing node... %s -> %s' % (str(next_func),node)
print 'lookahead %d' % self.model.lookahead
print 'FIRST BRIBED NODE! -> ', node, 'degree=', self.model.graph.degree(node), 'neighbors=', str(list(self.model.graph.neighbors_iter(node)))
# first node to bribe
self.model.add_bribed_node(node)
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
yield self.model.total_effort(), cov, cov_func_name
if self.model.total_effort() < max_effort:
for effort, coverage_frac, coverage_type in self.start_next_node(coverages, max_effort, next_func):
yield effort, coverage_frac, coverage_type
else:
# finish signal!
yield -1, None, None
def start_supernode_degree(self, coverages=[0.20], max_effort=999999999):
'''
Supernode active strategies only work with lookahead > 0.
'''
self.model.strategy = 'start_supernode_degree'
for effort, coverage_frac, coverage_type in self.start_supernode_order(coverages, max_effort, self.model.sorted_degrees_dec_iter(), 'start_supernode_degree'):
yield effort, coverage_frac, coverage_type
def start_supernode_random(self, coverages=[0.20], max_effort=999999999):
'''
Supernode active strategies only work with lookahead > 0.
'''
self.model.strategy = 'start_supernode_random'
for effort, coverage_frac, coverage_type in self.start_supernode_order(coverages, max_effort, self.model.random_node_order_iter(), 'start_supernode_random'):
yield effort, coverage_frac, coverage_type
def start_supernode_order(self, coverages=[0.20], max_effort=None, nlist=[], name=None):
'''
Active attacks, a rogue node is added, and then nodes in nlist are linked to the supernode.
Supernode active strategies only work with lookahead > 0.
'''
if len(coverages) < 1:
raise PrivacyAttackStrategiesException('Error: strategies need a non-empty listo of graph float coverages!')
rogue_node = str(random.random())[2:]
self.model.add_agent_node( rogue_node )
for node in nlist:
if self.debug:
print 'adding link from super node to node... %s -> %s' % (str(name),node)
print 'lookahead %d' % self.model.lookahead
if not self.model.graph.has_edge(rogue_node, node):
self.model.add_false_link(rogue_node, node)
else:
continue
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
if cov >= coverages[cov_func_name][0]:
while cov >= coverages[cov_func_name][0]:
yield self.model.total_effort(), coverages[cov_func_name][0], cov_func_name
coverages[cov_func_name] = coverages[cov_func_name][1:]
if len(coverages[cov_func_name]) == 0:
del coverages[cov_func_name]
break
if self.model.total_effort() >= max_effort or len(coverages)==0:
break
# finish signal!
yield -1, None, None
def start_supernode_greedy(self, coverages=[0.20], max_effort=999999999):
'''
Supernode active strategies only work with lookahead > 0.
'''
self.model.strategy = 'start_supernode_greedy'
return self.start_next_supernode(coverages, max_effort, self.model.max_unseen_degree_node)
def start_supernode_crawler(self, coverages=[0.20], max_effort=999999999):
'''
Supernode active strategies only work with lookahead > 0.
'''
self.model.strategy = 'start_supernode_crawler'
node = None
for n in self.model.random_node_order_iter():
node = n
break
# choose random node to start crawling...
#node = self.model.random_node()
for effort, coverage_frac, coverage_type in self.start_next_supernode(coverages, max_effort, self.model.max_unseen_degree_crawler_node, node):
yield effort, coverage_frac, coverage_type
def start_next_supernode(self, coverages=[0.20], max_effort=None, next_func=None, first_node=None):
'''
Supernode active strategies only work with lookahead > 0.
'''
if len(coverages) < 1:
raise PrivacyAttackStrategiesException('Error: strategies need a non-empty listo of graph float coverages!')
rogue_node = str(random.random())[2:]
self.model.add_agent_node( rogue_node )
if first_node:
node = first_node
if self.debug:
print 'adding link from super node to node... %s -> %s' % ('start_crawler()',node)
print 'lookahead %d' % self.model.lookahead
if not self.model.graph.has_edge(rogue_node, node):
self.model.add_false_link(rogue_node, node)
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
if cov >= coverages[cov_func_name][0]:
while cov >= coverages[cov_func_name][0]:
yield self.model.total_effort(), coverages[cov_func_name][0], cov_func_name
coverages[cov_func_name] = coverages[cov_func_name][1:]
if len(coverages[cov_func_name]) == 0:
del coverages[cov_func_name]
break
if self.model.total_effort() >= max_effort or len(coverages)==0:
# finish signal!
yield -1, None, None
return
nc = 0
while nc < self.model.graph.number_of_nodes():
node = next_func()
if self.debug:
print 'adding link from super node to node... %s -> %s' % (str(next_func),node)
print 'lookahead %d' % self.model.lookahead
if not self.model.graph.has_edge(rogue_node, node):
self.model.add_false_link(rogue_node, node)
else:
continue
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
if cov >= coverages[cov_func_name][0]:
while cov >= coverages[cov_func_name][0]:
yield self.model.total_effort(), coverages[cov_func_name][0], cov_func_name
coverages[cov_func_name] = coverages[cov_func_name][1:]
if len(coverages[cov_func_name]) == 0:
del coverages[cov_func_name]
break
if self.model.total_effort() >= max_effort or len(coverages)==0:
break
nc += 1
# finish signal!
yield -1, None, None
def start_generic(self, coverages=[0.20], max_effort=999999999, action_generator=None):
if len(coverages) < 1:
raise PrivacyAttackStrategiesException('Error: strategies need a non-empty list of graph float coverages!')
nc = 0
while nc < self.model.graph.number_of_nodes():
action = action_generator()
if self.debug:
print 'executing action %s... ' % (str(action))
print 'lookahead %d' % self.model.lookahead
action()
if self.debug:
print 'effort: %d' % self.model.total_effort()
for cov_func, cov_func_name in self.coverage_functions:
if not cov_func_name in coverages:
continue
cov = cov_func()
if self.debug:
print 'coverage %s: ' % (cov_func_name)
print ' %f' % (cov)
if cov >= coverages[cov_func_name][0]:
while cov >= coverages[cov_func_name][0]:
yield self.model.total_effort(), coverages[cov_func_name][0], cov_func_name
coverages[cov_func_name] = coverages[cov_func_name][1:]
if len(coverages[cov_func_name]) == 0:
del coverages[cov_func_name]
break
if self.model.total_effort() >= max_effort or len(coverages)==0:
break
nc += 1
# finish signal!
yield -1, None, None
|
