DEVELOPMENT... OpenML
Data
disclosure_x_noise

disclosure_x_noise

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by unknown
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Date unknown Please cite: Data Used in "A BAYESIAN APPROACH TO DATA DISCLOSURE: OPTIMAL INTRUDER BEHAVIOR FOR CONTINUOUS DATA" by Stephen E. Fienberg, Udi E. Makov, and Ashish P. Sanil Background: ========== In this paper we develop an approach to data disclosure in survey settings by adopting a probabilistic definition of disclosure due to Dalenius. Our approach is based on the principle that a data collection agency must consider disclosure from the perspective of an intruder in order to efficiently evaluate data disclosure limitation procedures. The probabilistic definition and our attempt to study optimal intruder behavior lead naturally to a Bayesian formulation. We apply the methods in a small-scale simulation study using data adapted from an actual survey conducted by the Institute for Social Research at York University. (See Sections 1-3 of the paper for details oF the model formulation and related issues.) The Data: ======== Our case study uses data from the survey data Elite Canadian Decision-Makers collected by the Institute for Social Research at York University. This survey was conducted in 1981 using telephone interviews and there were 1348 respondents, but many of these did not supply complete data. We have extracted data on 12 variables, each of which was measured on a 5-point scale: Civil-liberties: - --------------- C1 - Free speech is just not worth it. C2 - We have gone too far in pushing equal rights in this country. C3 - It is better to live in an orderly society than to allow people so much freedom. C5 - Free speech ought to be allowed for all political groups. Attitudes towards Jews: - ---------------------- A15 - Most Jews don't care what happens to people who are not Jews. A18 - Jews are more willing than others to use shady practices to get ahead. Canada-US relationship: - ---------------------- CUS1 - Ensure independent Canada. CUS5 - Canada should have free trade with the USA. CUS6 - Canada's way of life is influenced strongly by USA. CUS7 - Canada benefits from US investments. In addition, we have data on two approximately continuous variables: Personal information: - -------------------- Income - Total family income before taxes (with top-coding at \$80,000). Age - Based on year of birth. We transformed the original survey data as follows in order to create a database of approximately continuous variables: [A] We add categorical variables (all but income) to increase the number of levels. (When necessary we reversed the order of levels of a response to a question.) The new variables are defined as follows: Civil = C1 + C2 + C3 + (8 - C5) Attitude = A15 + A18 Can/US = (5 - CUS1) + CUS5 + (5 - CUS6) + CUS7 After we removed cases with missing observations and two cases involving young children, we had a data-base consisting of 662 observations. [B] In order to enhance continuity, we took the following measures: Age: We added normal distributed variates, with 0 mean and variance 4 to all observations. Income: We added uniform variates on the range of $0 - $10,000 to all incomes below $80,000. Since all cases of incomes exceeding $80,000 were lumped together in the survey, we simulated their values by means of a t(8) distribution. Drawing values from the upper 38% tail of t(8), we evaluated the values of income as $60,000 + 25,000*t(8). Other variables: We added normal distributed variates, with 0 mean and variance 0.5 to the variables. We assume that the agency releases information about all variables, except for Attitudes (towards Jews), which is unavailable to the intruder and is at the center of the intruder's investigation. We denote the released data by Z = (( z(i,j) )) with i=1,..,662; j=1,2,3,4. We assume that the intruder's data, X, are accurate and are related to Z via the following transformation: x(0,j) = z(i,j)*theta(i,j) + xi(j), where theta(i,j) is a bias removing parameter normally distributed with mean 1 and variance v(j), and xi(j) is normally distributed disturbance with 0 mean and variance sigma2(j). The following table provides the values of v(j), sigma2(j) used in the study: v(j) sigma2(j) Civil 0.1732 25 Can/US 0.1732 25 Age 0.1732 9 Income (in $10000's) 0.1732 4 We first generated several realizations of the above transformation on small subsets of the data to ascertain the impact of the process of the error on the data. In Table 4-1 in the paper we present 10 records the the intruder's accurate data, X, and the biased and corrupted released data, Z, which we obtained from one realization of the transformation. Section 4.2 of the paper contains details of the implementation of our Bayesian model. Data Used in the Computations: ============================= We conducted a complete simulation of the procedures for the complete set of 662 cases. We considered four different scenarios for the simulation. (The names of datasets used in each of the scenarios appear in brackets below. The datasets are appended to this text.) * The released data contains no bias or noise (i.e. v(j)=0 and sigma2(j)=0 for all j). [Z.DATA] * The released data contains only noise (i.e., v(j)=0 for all j and and $sigma2(j)$ as given in the above Table). [X_NOISE.DATA] * The released data contains only bias (i.e., sigma2(j)=0 for all j and v(j) as given in the above Table). [X_BIAS.DATA] * The released data contains both bias and noise (i.e., v(j) and sigma2(j) as given in the above Table). [X_TAMPERED.DATA] We took each individual in turn as the object of the intruder's efforts and carried out the calculations. Structure of the Datasets: - ------------------------- Each attached dataset consists of four space-separated columns containing the data on Age, Civil, Can/US and Income ($) respectively. Dataset: X_NOISE Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

4 features

Income (target)numeric662 unique values
0 missing
Agenumeric662 unique values
0 missing
Civilnumeric662 unique values
0 missing
Can/USnumeric662 unique values
0 missing
Show all 4 features

107 properties

NumberOfInstances
662
Number of instances (rows) of the dataset.
NumberOfFeatures
4
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
4
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
-31980.07
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.85
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
72812.15
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.29
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
29961.28
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.25
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
18220.65
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.14
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
7497.03
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.06
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
9.89
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.18
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
4.26
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.02
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
11.97
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.08
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.91
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.11
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
30.29
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.23
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
11.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.67
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
54619.7
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.28
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
22474.89
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Regression on disclosure_x_noise
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: Income
Supervised Regression on disclosure_x_noise
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: Income
Supervised Regression on disclosure_x_noise
0 runs - estimation_procedure: 33% Holdout set - target_feature: Income
Clustering on disclosure_x_noise
0 runs
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Clustering on disclosure_x_noise
0 runs - estimation_procedure: 50 times Clustering
Define a new task