mvn install:install-file -Dfile=./openmarkov/OpenMarkov-0.4.0.jar -DgroupId=org.openmarkov -DartifactId=org.openmarkov.full -Dversion=0.4.0-SNAPSHOT -Dpackaging=jar
This project implements a network of bayesian ensembles that specificly makes use of the federated nature of the data to creates its ensembles. It uses two libraries: n-party scalar-product: https://github.com/MaastrichtU-CDS/n-scalar-product-protocol VertiBayes: https://github.com/MaastrichtU-CDS/vertibayes
A scientific article has been presented at the FLTA 2023 conference in Tartu. The proceedings are currently being prepared for publication on IEEE.
The privacy concerns are similar to VertiBayes this means the following:
This implementation relies on the n-party scalar product protocol mentioned earlier, and as such relies on a trusted third party, a different scalar product protocol could work without a trusted third party.
The output of this implementation is a bayesian network. It is important to note that a bayesian network reveals P( X=xi|Y=yi) for each of it's attributes. Combined with knowledge about the population size this means that publishing a bayesian network can reveal the counts of certain attribute values, as well as the counts of certain combinations. This information can potentially be used to reconstruct the original database. Because of this it is important to make sure when publishing the bayesian network that the population size is not released to untrusted parties.
It is important to note that repeat attempts to build a network with different predefined bins & different predefined network structures can be combined to reveal more information than a single attempt, making it easier to rebuild the original data. To protect against this good governance should be used. For example, only allow automatically generated structures & bins, ensuring each attempt results in the same network. Or by ensuring that only the best performing bayesian network is published, while the other attempts are kept secret.
This project uses Spring boot at its basis. The following properties are needed:
servers=<list of urls for the other servers>
secretServer=<url to the commodity server>
datapath=<path to a csv containing local data>
server.port=<port to be used>
server=<server id>
A dockerFile is included to wrap the project for use in vantage6.
The data can be presented in three formats:
- A weka .arff
- A .parquet file
- A .csv file
The vantage6 wrapper assumes a .csv file. Work is currently being done to allow more flexibility within the wrapper. vantage6/vantage6#398
When using a csv it needs to use the following format. The top row is assumed to contain the typing of the attributes (bool, string, numeric, real). The second row is assumed to be attribute ID's. We also assume the first collumn to contain ID's. The assumption is that the first collumn contains the recordId's.
It is assumed unknown data has the value of '?'
It is assumed values do not contain spaces, similarly it is assumed that the various keywords in a WEKA bif file are not used, as well as those used in a WEKA arff file.
Lastly "All" is a reserved keyword for a bin that contains all possible values for a given attribute.
To handle a Hybrid split in your data include an attributecolumn in all relevant datasets named "locallyPresent" with " bool" as it's type. Locally available data should have the value "TRUE". Missing records are then inserted as a row that has the value "FALSE" for this attribute. This should be handled in a preprocessing step.
In the request one can indicate if the models need to be made in a hybrid manner. If hybrid is set to false the models will only look at local data, if hybrid is set to false it'll look at all available data for locally.
For example; in a horizontal split; "hybrid:"false" will result in 2 different models based on the local data.
"hybrid:"false" will result in two models that are exactly the same.
The only exception to this is the class-label, which will always be treated as a hybrid value.
Important to note; datasets still need to have the same ordering for their records. Missing local records can be empty aside from the locallyPresent attribute. It is assumed that recordlinkage is handled in a preprocessing step as well.
createEnsemble: This method creates an ensemble of bayesian networks. It can either use predefined structures or determine them themselves using K2. It will also return the AUC for the ensemble using k-fold cross validation.
The request for createEnsemble looks as follows:
{
"target" : "x1",
"networks" : {
"2" : [ {
"parents" : [ ],
"name" : "x1",
"type" : "string",
"probabilities" : [ ],
"bins" : [ ]
}, {
"parents" : [ "x1" ],
"name" : "x3",
"type" : "string",
"probabilities" : [ ],
"bins" : [ ]
} ]
},
"binned" : [ {
"parents" : [ ],
"name" : "x1",
"type" : "numeric",
"probabilities" : [ ],
"bins" : [ {
"upperLimit" : "1.5",
"lowerLimit" : "0.5"
}, {
"upperLimit" : "0.5",
"lowerLimit" : "-0.5"
} ]
}, {
"parents" : [ ],
"name" : "x2",
"type" : "numeric",
"probabilities" : [ ],
"bins" : [ {
"upperLimit" : "1.5",
"lowerLimit" : "0.5"
}, {
"upperLimit" : "0.5",
"lowerLimit" : "-0.5"
} ]
}, {
"parents" : [ ],
"name" : "x3",
"type" : "string",
"probabilities" : [ ],
"bins" : [ ]
} ],
"hybrid" : false,
"minPercentage" : 0,
"folds" : 1,
"trainStructure":true
}
Binned is used to indicate bins for relevant nodes. If these are not indicated they will be determined automatically at which point a maximum of 10 bins will be made, and a minimum of 1 bin. Each bin will attempt to pick the smallest unique values that contain at least 10 individuals and 10% of the population. This is the default setting. It is also possible to create bins a minimum of 20%, 25%, 30% or 40% of the population. Other settings are not possible. If the current bin cannot be made large enough to achieve this it will be merged with the last bin. This percentage is set using minPercentage.
Networks can be used to predefine network structures. They are assigned to specific parties as key-value pairs, where the key corresponds to the parties ID. If no network is provided K2 is used to determine the local structure.
Within vantage6 the key used to indicate a party will correspond to the node ID. The "trainStructure" flag can be used to further train the network structure even if a predefined structure is given.
Hybrid can be used to indicate if there are hybrid attributes
{'networks':
['<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n<ProbModelXML formatVersion="0.2.0">\n <ProbNet type="BayesianNetwork">\n <DecisionCriteria>\n <Criterion name="---" unit="---"/>\n </DecisionCriteria>\n <Properties/>\n <Variables>\n <Variable name="x3" role="chance" type="finiteStates">\n <States>\n <State name="0"/>\n <State name="1"/>\n </States>\n </Variable>\n <Variable name="x1" role="chance" type="finiteStates">\n <States>\n <State name="0"/>\n <State name="1"/>\n </States>\n </Variable>\n </Variables>\n <Links>\n <Link directed="true">\n <Variable name="x3"/>\n <Variable name="x1"/>\n </Link>\n </Links>\n <Potentials>\n <Potential role="conditionalProbability" type="Table">\n <Variables>\n <Variable name="x3"/>\n </Variables>\n <Values>0.402009799020098 0.597990200979902</Values>\n </Potential>\n <Potential role="conditionalProbability" type="Table">\n <Variables>\n <Variable name="x1"/>\n <Variable name="x3"/>\n </Variables>\n <Values>0.7534195473762746 0.24658045262372544 0.32962715265005854 0.6703728473499415</Values>\n </Potential>\n </Potentials>\n </ProbNet>\n <InferenceOptions>\n <MulticriteriaOptions>\n <SelectedAnalysisType>UNICRITERION</SelectedAnalysisType>\n <Unicriterion>\n <Scales>\n <Scale Criterion="---" Value="1.0"/>\n </Scales>\n </Unicriterion>\n <CostEffectiveness>\n <Scales>\n <Scale Criterion="---" Value="1.0"/>\n </Scales>\n <CE_Criteria>\n <CE_Criterion Criterion="---" Value="Cost"/>\n </CE_Criteria>\n </CostEffectiveness>\n </MulticriteriaOptions>\n </InferenceOptions>\n</ProbModelXML>\n',
'<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n<ProbModelXML formatVersion="0.2.0">\n <ProbNet type="BayesianNetwork">\n <DecisionCriteria>\n <Criterion name="---" unit="---"/>\n </DecisionCriteria>\n <Properties/>\n <Variables>\n <Variable name="x1" role="chance" type="finiteStates">\n <States>\n <State name="0"/>\n <State name="1"/>\n </States>\n </Variable>\n <Variable name="x2" role="chance" type="finiteStates">\n <States>\n <State name="0"/>\n <State name="1"/>\n </States>\n </Variable>\n </Variables>\n <Links>\n <Link directed="true">\n <Variable name="x1"/>\n <Variable name="x2"/>\n </Link>\n </Links>\n <Potentials>\n <Potential role="conditionalProbability" type="Table">\n <Variables>\n <Variable name="x1"/>\n </Variables>\n <Values>0.4934006599340066 0.5065993400659934</Values>\n </Potential>\n <Potential role="conditionalProbability" type="Table">\n <Variables>\n <Variable name="x2"/>\n <Variable name="x1"/>\n </Variables>\n <Values>0.8013171225937183 0.19868287740628165 0.19587527136372607 0.8041247286362739</Values>\n </Potential>\n </Potentials>\n </ProbNet>\n <InferenceOptions>\n <MulticriteriaOptions>\n <SelectedAnalysisType>UNICRITERION</SelectedAnalysisType>\n <Unicriterion>\n <Scales>\n <Scale Criterion="---" Value="1.0"/>\n </Scales>\n </Unicriterion>\n <CostEffectiveness>\n <Scales>\n <Scale Criterion="---" Value="1.0"/>\n </Scales>\n <CE_Criteria>\n <CE_Criterion Criterion="---" Value="Cost"/>\n </CE_Criteria>\n </CostEffectiveness>\n </MulticriteriaOptions>\n </InferenceOptions>\n</ProbModelXML>\n'],
'aucs': {'0': 0.78, '1': 0.78}}
The repsonse contains the network in the bif format used by openMarkov. The AUC is listed per class label.
Crossfold validation is automaticly executed if folds >1. The maximum number of folds is 10.
The wrapper uses the following request:
nodes=<Data-owner nodes>
networks=<key-value pairs, listing predefined network structures per dataparty, the key indicates the relevant node>
binned=<Predefined bins>
COMMODITYSERVER=<Organisation selected to play the role of commodity server>
minPercentage=<minimum percentage used for binning scheme, 0.1, 0.25, 0.3 or 0.4>
target=<the class variable>
hybrid=<Boolean indicating if the ensembles may make use of hybrid variables, or act as if everything is purely based on local information.>
folds=<number of folds for crossfold validation, max 10, min 1>
task = self.client.task.create(collaboration=collaboration,
organizations=[commodity_node],
name=NAME, image=IMAGE, description=NAME,
input={'method': 'bayesianEnsemble', 'master': True,
'args': [nodes, target, networks, binned, minpercentage, hybrid, folds]})