def find_used_contexts(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT DISTINCT ?context_subject ?ContextType
WHERE {
?context_subject rdf:type/rdfs:subClassOf* pv2:Context .
?context_subject rdf:type ?ContextType .
}
""",
initNs=NSUtil.get_binding_namespaces())
# ns = dict(pv=Namespace("<https://ontology.hviidnet.com/2020/01/03/privacyvunl.ttl#>"))
ro = inputModel_temp.query(q)
used_contexts = {}
for row in ro:
used_contexts[row[0]] = row[1]
return used_contexts
def find_all_data_creates_from_data_types(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT DISTINCT ?transformation ?datainput ?outputType
WHERE {
?transformation pv2:creates ?output .
?output rdf:type ?outputType .
?datainput pv:feeds ?transformation .
# ?datainput rdf:type ?datainput_type .
# ?datainput_type rdf:type/rdfs:subClassOf pv:Data .
?outputType rdf:type/rdfs:subClassOf pv:Data .
}
""",
initNs=NSUtil.get_binding_namespaces())
# ns = dict(pv=Namespace("<https://ontology.hviidnet.com/2020/01/03/privacyvunl.ttl#>"))
ro = inputModel_temp.query(q)
domain_data_found_using_data = {}
for row in ro:
if not row[1] in domain_data_found_using_data:
domain_data_found_using_data[row[1]] = []
domain_data_found_using_data[row[1]].append(row[2])
return domain_data_found_using_data
def find_used_data_types(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT DISTINCT ?inputName ?individualData ?datatypes
WHERE {
?individualData rdf:type owl:NamedIndividual .
?inputName rdf:type ?individualData .
}
""",
initNs=NSUtil.get_binding_namespaces())
# ns = dict(pv=Namespace("<https://ontology.hviidnet.com/2020/01/03/privacyvunl.ttl#>"))
ro = inputModel_temp.query(q)
used_data_types = {}
for row in ro:
used_data_types[row[0]] = row[1]
return used_data_types
def _find_spatial_resolution_for_transformation(self):
transformation_temp = self.transformation
transformation_temp.parse(self.domain_path)
transformation_temp.parse(self.extention_ontology_path)
transformation_temp.parse(self.base_ontology_path)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT ?data ?transformationName ?input ?output
WHERE {
?dataTypes rdfs:subClassOf pv:Data .
?data rdf:type ?dataTypes .
?req pv:feeds ?data .
?req rdf:type pv2:Constraint .
?req pv:feeds ?transformationName .
?transformationName rdf:type pv:Transformation .
?srTypes rdf:type pv2:SpatialResolution .
?srTypes pv2:spatialInput ?input .
?srTypes pv2:spatialOutput ?output .
?req pv:feeds ?srTypes
}
""",
initNs=NSUtil.get_binding_namespaces())
ro = transformation_temp.query(
q) #, initBindings={'transformation': transformation_name}
spatial_relations = {}
for row in ro:
if not row[0] in spatial_relations: spatial_relations[row[0]] = []
spatial_relations[row[0]].append(
SpatialRelation(row[0], row[1], row[2], row[3]))
return spatial_relations
def _find_output_for_template(self):
model_temp = self.transformation
model_temp.parse(self.domain_path)
model_temp.parse(self.base_ontology_path)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT ?outputSubject ?dataType ?outputDataType
WHERE {
?dataType rdfs:subClassOf pv:Data .
?outputSubject rdf:type ?dataType .
?transformationName rdf:type pv:Transformation .
?transformationName pv:feeds ?outputSubject .
?outputSubject rdf:type ?outputDataType .
?outputDataType rdf:type ?dataType .
}
""",
initNs=NSUtil.get_binding_namespaces())
ro = model_temp.query(q)
dataTypes = []
for row in ro:
return row[0], row[1], row[2]
def find_all_input_data_old(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT DISTINCT ?context ?inputName ?individualData ?resolution ?templateCount ?context_type
WHERE {
?individualData rdf:type owl:NamedIndividual .
?inputName rdf:type ?individualData .
?dataTypes rdfs:subClassOf pv:Data .
?inputName rdf:type ?dataTypes .
?context rdf:type/rdfs:subClassOf* pv2:Context .
?context pv2:has* ?directDataInputName .
?context rdf:type ?context_type .
#Finds datatypes supported by domain
?directDataInputName pv:feeds* ?inputName .
#Find transformation count for datainput if it is set
OPTIONAL {
?inputName pv2:TemplateCount ?templateCount .
}.
OPTIONAL {
?inputName pv2:TemporalResolution ?resolution .
}
FILTER ( !bound(?templateCount) || ?templateCount < ?max_count )
}
""",
initNs=NSUtil.get_binding_namespaces()
# ,
# initNs = {}
)
ro = inputModel_temp.query(
q, initBindings={'?max_count': self.max_transformation_count})
supported_data_types = {}
for row in ro:
if not row[0] in supported_data_types:
supported_data_types[row[0]] = []
template_count = 0 if row[4] is None else int(row[4])
resolution = None if row[3] is None else float(row[3])
supported_data_types[row[0]].append(
Data(row[2],
resolution,
subject_name=row[1],
template_count=template_count,
context=row[5],
context_subject=row[0]))
return supported_data_types
def find_contexts(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
ro = inputModel_temp.query("""
SELECT DISTINCT ?sub_class_subject
WHERE {
?class_types rdfs:subClassOf* pv2:Context .
?sub_class_subject rdf:type ?class_types .
}
""",
initNs=NSUtil.get_binding_namespaces())
contexts = []
for row in ro:
contexts.append(row[0])
# import pdb; pdb.set_trace()
return contexts
def _find_time_resolution_for_transformation(self):
transformation_temp = self.transformation
transformation_temp.parse(self.domain_path)
transformation_temp.parse(self.extention_ontology_path)
transformation_temp.parse(self.base_ontology_path)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT ?req ?data ?temporalResolution ?spatialResolution ?transformationName ?trTypes ?input ?output ?trName
WHERE {
?dataTypes rdfs:subClassOf pv:Data .
?data rdf:type ?dataTypes .
?req pv:feeds ?data .
?req rdf:type pv2:Constraint .
?req pv2:TemporalResolution ?temporalResolution .
# ?req pv2:SpatialResolution ?spatialResolution .
?req pv:feeds ?transformationName .
?transformationName rdf:type pv:Transformation .
?trTypes rdfs:subClassOf pv2:TimeResolution .
?trName rdf:type ?trTypes .
?trName pv2:TimeInput ?input .
?trName pv2:TimeOutput ?output .
?req pv:feeds ?trName
}
""",
initNs=NSUtil.get_binding_namespaces())
ro = transformation_temp.query(
q) #, initBindings={'transformation': transformation_name}
dataTypes = []
for row in ro:
dataTypes.append(
ResolutionForTransformation(row[0], row[1],
float(row[2].value),
row[3], row[4], row[5],
float(row[6].value),
float(row[7].value), row[8]))
return dataTypes
def find_all_data_types_for_domain_data(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
q = rdflib.plugins.sparql.prepareQuery(
"""
SELECT DISTINCT ?individualData ?inputName ?individualData_subData
WHERE {
?individualData rdf:type owl:NamedIndividual .
?inputName rdf:type ?individualData .
?dataTypes rdfs:subClassOf pv:Data .
?inputName rdf:type ?dataTypes .
?individualData_subData rdf:type owl:NamedIndividual .
?inputName_subData rdf:type ?individualData_subData .
?dataTypes_subData rdfs:subClassOf pv:Data .
?inputName_subData rdf:type ?dataTypes_subData .
?inputName pv:feeds* ?feedsRelations .
?feedsRelations rdf:type ?individualData_subData .
}
""",
initNs=NSUtil.get_binding_namespaces())
# ns = dict(pv=Namespace("<https://ontology.hviidnet.com/2020/01/03/privacyvunl.ttl#>"))
ro = inputModel_temp.query(q)
domain_data_found_using_data = {}
for row in ro:
if not row[1] in domain_data_found_using_data:
domain_data_found_using_data[row[1]] = []
domain_data_found_using_data[row[1]].append(row[2])
return domain_data_found_using_data
def _find_domain_supported_data_streams(self):
#all inputs needs to be in the inputModel in order to use the template.
model = Graph()
#Load domain ontology
model.parse(self.domain_path)
#Load base ontology
model.parse(self.base_ontology_path)
ro = model.query("""
SELECT ?data
WHERE {
?dataTypes rdfs:subClassOf pv:Data .
?data rdf:type ?dataTypes .
}
""",
initNs=NSUtil.get_binding_namespaces())
dataTypes = []
for row in ro:
dataTypes.append(row[0])
return dataTypes
def __find_domain_supported_contexts(self):
model = Graph()
model.parse(self.domain_path)
q = rdflib.plugins.sparql.prepareQuery("""
SELECT ?individualData
WHERE {
?individualData rdfs:subClassOf* pv2:Context .
}
""",
initNs = NSUtil.get_binding_namespaces()
# ,
# initNs = {}
)
ro = model.query(q)
supported_contexts = []
for row in ro:
supported_contexts.append(row[0])
return supported_contexts
def _find_output_for_template(self):
model_temp = self.privacy_attack
model_temp.parse(self.domain_path)
model_temp.parse(self.base_ontology_path)
# import pdb; pdb.set_trace()
q = rdflib.plugins.sparql.prepareQuery("""
SELECT ?templateSubject ?outputSubject ?description ?privacyRiskScore ?privacyStrategy
WHERE {
?templateSubject rdf:type pv:PrivacyAttack .
?templateSubject pv:creates ?outputSubject .
?outputSubject rdf:type pv:PrivacyRisk .
OPTIONAL {
?outputSubject pv2:description ?description .
} .
OPTIONAL {
?outputSubject pv2:privacyRiskScore ?privacyRiskScore .
}
OPTIONAL {
?outputSubject pv2:privacyStrategy ?privacyStrategy .
}
}
""",
initNs = NSUtil.get_binding_namespaces()
)
ro = model_temp.query(q)
privacyStrategies = []
for row in ro:
privacy_risk_score = 1
if row[3] is not None:
privacy_risk_score = row[3].value
if row[4] is not None:
privacyStrategies.append(row[4].value)
if row is not None:
return row[0], self.PRIVVULN.PrivacyRisk, row[1], row[2], privacy_risk_score, privacyStrategies
def find_context_structure(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
ro = inputModel_temp.query("""
SELECT ?sub_class_types ?sub_class_subject ?root_type ?root_subject
WHERE {
?root_subject rdf:type ?root_type .
?root_subject pv:star ?sub_class_subject .
?sub_class_types rdfs:subClassOf* pv2:Context .
?sub_class_subject rdf:type ?sub_class_types .
}
""",
initNs=NSUtil.get_binding_namespaces())
contexts = {}
for row in ro:
contexts[row[1]] = ContextInput(row[0], row[1], row[2], row[3])
return contexts
def _find_context_roots(self,input_model):
input_model_temp = copy.deepcopy(input_model)
input_model_temp.parse(self.domain_path)
input_model_temp.parse(self.extention_ontology_path)
input_model_temp.parse(self.base_ontology_path)
q = rdflib.plugins.sparql.prepareQuery("""
SELECT ?context
WHERE {
?context rdf:type/rdfs:subClassOf* pv2:Context .
FILTER NOT EXISTS { ?subject pv2:star ?context}
}
""",
initNs = NSUtil.get_binding_namespaces()
)
ro = input_model_temp.query(q)
context_roots = []
for row in ro:
context_roots.append(row[0])
return context_roots
def find_all_domain_types_from_input_data_old(self, inputModel):
inputModel_temp = copy.deepcopy(inputModel)
inputModel_temp.parse(self.domain_url)
inputModel_temp.parse(self.extention_ontology_url)
inputModel_temp.parse(self.base_ontology_url)
q = rdflib.plugins.sparql.prepareQuery(
"""
# SELECT DISTINCT ?inputName ?individualData ?inputName_subData ?individualData_subData ?templateCount ?resolution ?context ?contextType
SELECT DISTINCT ?inputName ?individualData ?inputName_subData ?individualData_subData ?templateCount ?resolution ?context ?contextType
WHERE {
?individualData rdf:type owl:NamedIndividual .
?inputName rdf:type ?individualData .
?inputName rdf:type/rdfs:subClassOf pv:Data .
# ?inputName rdf:type ?dataTypes .
?individualData_subData rdf:type owl:NamedIndividual .
?inputName_subData rdf:type ?individualData_subData .
?dataTypes_subData rdfs:subClassOf pv:Data .
?inputName_subData rdf:type ?dataTypes_subData .
?inputName pv:feeds* ?inputName_subData .
?context pv2:has ?inputName .
?context rdf:type/rdfs:subClassOf* pv2:Context .
?context rdf:type ?contextType .
#Find transformation count for datainput if it is set
OPTIONAL {
?inputName_subData pv2:TemplateCount ?templateCount .
}.
OPTIONAL {
?inputName_subData pv2:TemporalResolution ?resolution .
}
FILTER ( !bound(?templateCount) || ?templateCount < ?max_count )
}
""",
initNs=NSUtil.get_binding_namespaces())
seconds = time.time()
ro = inputModel_temp.query(
q, initBindings={'?max_count': self.max_transformation_count})
domain_data_found_using_data = {}
for row in ro:
print("Loop:", time.time() - seconds)
if not row[6] in domain_data_found_using_data:
domain_data_found_using_data[row[6]] = {}
if not row[1] in domain_data_found_using_data[row[6]]:
domain_data_found_using_data[row[6]][row[1]] = []
template_count = 0 if row[4] is None else int(row[4])
resolution = None if row[5] is None else float(row[5])
domain_data_found_using_data[row[6]][row[1]].append(
Data(row[3],
resolution,
subject_name=row[2],
template_count=template_count,
base_subject_name=row[0],
spatial_resolutions=row[7]))
# domain_data_found_using_data[row[1]].append(row[2])
return domain_data_found_using_data
