def score(self, key_data, sensitive_data):
ref_key_attributes = closest_neighbors(self.synthetic_dict.keys(),
key_data)
ref_sensitive_attributes = []
for key in ref_key_attributes:
ref_sensitive_attributes.extend(self.synthetic_dict[key])
return count_frequency(ref_sensitive_attributes, sensitive_data)
def score(self, key_data, sensitive_data):
"""Score based on the belief of the attacker, in the form P(sensitive_data|key|data).
Args:
key_data (tuple):
The key data.
sensitive_data (tuple):
The sensitive data.
Returns:
float or None:
The frequency of the correct sensitive entry.
Returns `0` if the key is not in the data.
"""
if key_data in self.synthetic_dict:
return count_frequency(self.synthetic_dict[key_data], sensitive_data)
else:
return 0
def score(self, key_data, sensitive_data):
"""Score based on the belief of the attacker, in the form P(sensitive_data|key|data).
Args:
key_data (tuple):
The key data.
sensitive_data (tuple):
The sensitive data.
Returns:
float or None:
The frequency of the correct sensitive entry.
"""
ref_key_attributes = closest_neighbors(self.synthetic_dict.keys(), key_data)
ref_sensitive_attributes = []
for key in ref_key_attributes:
ref_sensitive_attributes.extend(self.synthetic_dict[key])
return count_frequency(ref_sensitive_attributes, sensitive_data)
def score(self, key_data, sensitive_data):
if key_data in self.synthetic_dict:
return count_frequency(self.synthetic_dict[key_data],
sensitive_data)
else:
return 0
