Comparing bit vectors generated by CreateAS16

Description

Comparing all elements of two vectors of records with each other using Armknechts & Schnells methods "create" and "compare".

Usage

CompareAS16(IDA, dataA, IDB, dataB, password, t = 0.85)

Arguments

Details

Two bit vectors generated by CreateAS16 are compared as described in the original publication.

Value

The function returns a data.frame with four columns containing all ID-pairs of all bit vectors, the estimated Tanimoto similarity and the classification (links/non-links).

Source

Armknecht, F., Schnell, R. (unpublished): Privacy Preserving Record Linkage Based on Bloom Filters and Codes. Working Paper.

See Also

CreateAS16

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create Bloom Filter
testData <- CreateBF(ID = testData$V1, testData$V7, k = 20, padding = 1,
  q = 2, l = 1000, password = "(H]$6Uh*-Z204q")

# Optional: Create the new Bloom filter, the output of this function is just
# to see the created bit vectors, it is not the input of CompareAS16.
testAS <- CreateAS16(testData$ID, testData$CLKs, password = "khäuds")

# Compare bit vectcors, input is not the out put of CreateAS16,
# but the original Bloom Filters. CreateAS16 is executated in CompareAS16.
res <- CompareAS16(testData$ID, testData$CLKs, testData$ID, testData$CLKs,
  password = "khäuds", t = 0.85)

Create Encrypted Statistical Linkage Keys

Description

Creates ESLs (also known as 581-Keys), which are the hashed combination of the full date of birth and sex and subsets of first and last names.

Usage

Create581(ID, data, code, password)

Arguments

Details

The original implementation uses the second and third position of the first name, the second, third and fifth position of the last name and full date of birth and sex as an input for an HMAC. This would be akin to using code = list(c(2, 3), c(2, 3, 5), 0, 0). In this implementation, the positions of the subsets can be customized.

Value

A data.frame containing IDs and the corresponding Encrypted Statistical Linkage Keys.

Source

Karmel, R., Anderson, P., Gibson, D., Peut, A., Duckett, S., Wells, Y. (2010): Empirical aspects of record linkage across multiple data sets using statistical linkage keys: the experience of the PIAC cohort study. BMC Health Services Research 41(10).

See Also

CreateALC, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Encrypt data
res <- Create581(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  code = list(0, 0, c(2, 3), c(2, 3, 5)),
  password = "(H]$6Uh*-Z204q")

# Code: 0 means the whole string is used, 
# c(2, 3) means the second and third letter of the string is used

Anonymous Linkage Codes (ALCs)

Description

Creates ALCs from clear-text data by creating soundex phonetics for first and last names and concatenating all other identifiers. The resulting code is encrypted using SHA-2. The user can decide on which columns the soundex phonetic is applied.

Usage

CreateALC(ID, data, soundex, password)

Arguments

Value

A data.frame containing IDs and the corresponding Anonymous Linkage Codes.

Source

Herzog, T. N., Scheuren, F. J., Winkler, W. E. (2007): Data Quality and Record Linkage Techniques. Springer.

See Also

Create581, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Encrypt data, use Soundex for names
res <- CreateALC(ID = testData$V1,   
  data = testData[, c(2, 3, 7, 8)],
  soundex = c(0, 0, 1, 1),  
  password = "$6Uh*-Z204q")
  

Creating Records with Armknechts method create

Description

This method generates a new bit vector out of an existing Bloom Filter. Building and comparisons are both possible with CompareAS16.

Usage

CreateAS16(ID, data, password)

Arguments

Value

A character vector containing bit vectors created as described in the original publication.

Source

Armknecht, F., Schnell, R. (unpublished): Privacy Preserving Record Linkage Based on Bloom Filters and Codes. Working Paper.

See Also

CompareAS16, CreateBF, CreateCLK

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create Bloom Filter
testData <- CreateBF(ID = testData$V1, testData$V7, k = 20, padding = 1,
  q = 2, l = 1000, password = "(H]$6Uh*-Z204q")

# Create the new Bloom Filter
testAS <- CreateAS16(testData$ID, testData$CLKs, password = "khäuds")

Bloom Filter Encoding

Description

Creates Bloom filters for each row of the input data by splitting the input into q-grams which are hashed into a bit vector.

Usage

CreateBF(ID, data, password, k = 20, padding = 1, qgram = 2, lenBloom = 1000)

Arguments

Value

A data.frame containing IDs and the corresponding bit vector.

Source

Schnell, R., Bachteler, T., Reiher, J. (2009): Privacy-preserving record linkage using Bloom filters. BMC Medical Informatics and Decision Making 9: 41.

See Also

CreateCLK, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Encode data
BF <- CreateBF(ID = testData$V1, data = testData$V7,
  k = 20, padding = 1, q = 2, l = 1000,
  password = "(H]$6Uh*-Z204q")

Balanced Bloom Filter Encoding

Description

Creates CLKs with constant Hamming weights by adding a negated copy of the binary input vector which is then permutated.

Usage

CreateBalancedBF(ID, data, password)

Arguments

Value

A data.frame containing IDs and the corresponding Balanced Bloom Filter.

References

Berger, J. M. (1961): A Note on Error Detection Codes for Asymmetric Channels. In: Information and Control 4: 68–73.

Knuth, Donald E. (1986): Efficient Balanced Codes. In: IEEE Transactions on Information Theory IT-32 (1): 51–53.

Schnell, R., Borgs, C. (2016): Randomized Response and Balanced Bloom Filters for Privacy Preserving Record Linkage. IEEE International Conference on Data Mining (ICDM 2016), Barcelona.

See Also

CreateBF, CreateBitFlippingBF, CreateCLK, CreateDoubleBalancedBF, CreateEnsembleCLK, CreateMarkovCLK, CreateRecordLevelBF, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create bit vectors e.g. with CreateBF
testData <- CreateBF(ID = testData$V1,
  testData$V7, k = 20, padding = 1, q = 2,
  l = 1000, password = "(H]$6Uh*-Z204q")

# Create Balanced Bloom Filters
BB <- CreateBalancedBF(ID = testData$ID, data = testData$CLKs,
  password = "hdayfkgh")

Permanent Randomized Response Bloom Filters

Description

Applies Permanent Randomized Response to flip bits of the bit vectors given.

Usage

CreateBitFlippingBF(data, password, f)

Arguments

Details

The randomized response technique is used on each bit position B[i] of a Bloom filter B. B[i] is set to one or zero with a probability of 1/2 * f for each outcome. The bit position remains unchanged with a probability of 1 - f, where 0 <= f <= 1.

Value

A data.frame containing IDs and the corresponding bit vector.

Source

Schnell, R., Borgs, C. (2016): Randomized Response and Balanced Bloom Filters for Privacy Preserving Record Linkage. IEEE International Conference on Data Mining (ICDM 2016), Barcelona.

See Also

CreateBF, CreateCLK, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <-read.csv(testFile, head = FALSE,
  sep = "\t", colClasses = "character")

## Encode data into Bloom Filters
BF <- CreateBF(ID = testData$V1, data = testData$V7,
  k = 20, padding = 1, q = 2, l = 1000,
  password = "(H]$6Uh*-Z204q" )

# Create Permanent Randomized Response Bloom Filter
RR <- CreateBitFlippingBF(BF, password = "l+kfdj1J", f = 0.1)

Cryptographic Long-term Keys (CLKs)

Description

Each column of the input data.frame is hashed into a single additive Bloom filter.

Usage

CreateCLK(ID, data, password, k = 20,
  padding =  as.integer(c(0)),
  qgram =  as.integer(c(2)), lenBloom = 1000)

Arguments

Value

A data.frame containing IDs and the corresponding bit vector.

Source

Schnell, R. (2014): An efficient Privacy-Preserving Record Linkage Technique for Administrative Data and Censuses. Journal of the International Association for Official Statistics (IAOS) 30: 263-270.

See Also

CreateBF, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <-read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

## Encode data
CLK <- CreateCLK(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  k = 20, padding = c(0, 0, 1, 1),
  q = c(1, 1, 2, 2), l = 1000,
  password = c("HUh4q", "lkjg", "klh", "Klk5"))

Double Balanced Bloomfilter Encoding

Description

Double balanced Bloom Filter are created by first creating balanced Bloom Filters, see CreateBalancedBF, negating the whole data set and shuffling each Bloom Filter.

Usage

CreateDoubleBalancedBF(ID, data, password)

Arguments

Value

A data.frame containing IDs and the corresponding double balanced bit vector.

References

Schnell, R. (2017): Recent Developments in Bloom Filter-based Methods for Privacy-preserving Record Linkage. Curtin Institute for Computation, Curtin University, Perth, 12.9.2017.

See Also

CreateBalancedBF, CreateBF, CreateCLK, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <-read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create bit vectors e.g. with CreateBF
testData <- CreateBF(ID = testData$V1,
  testData$V7, k = 20, padding = 1, q = 2,
  l = 1000, password = "(H]$6Uh*-Z204q")

# Create Double Balanced Bloom Filters
DBB <- CreateDoubleBalancedBF(ID = testData$ID, data = testData$CLKs,
  password = "hdayfkgh")

Combine multiple independent CLKs using a simple majority rule

Description

Creates multiple CLKs which are combined using a simple majority rule.

Usage

CreateEnsembleCLK(ID, data, password, NumberOfCLK = 1 , k = 20,
  padding = as.integer(c(0)), qgram = as.integer(c(2)),
  lenBloom = 1000)

Arguments

Details

Creates a set number of independent CLKs for each record of the input data.frame and combines them using a simple majority rule. The bit positions B[i] in the final CLK of the length B are set to B[i] = 1 if more than half of the independent CLKs bit positions B[i] have a value of one. Otherwise the final bit position is zero.

Value

A data.frame containing IDs and the corresponding ensemble bit vector.

References

Kuncheva, L. (2014): Combining Pattern Classifiers: Methods and Algorithms. Wiley.

See Also

CreateBF, CreateCLK, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <-read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")
## Not run: 
# Create Ensemble CLK
EnsembleCLK <- CreateEnsembleCLK(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  k = 20, padding = c(0, 0, 1, 1),
  q = c(1, 2, 2, 2), l = 1000,
  password = c("HUh4q", "lkjg", "klh", "Klk5"),
  NumberOfCLK = 5)

## End(Not run)

Create CLKs with Markov Chain-based transition matrix

Description

Builds CLKs encoding additional bigrams based on the transition probabilities as estimated by a Markov Chain.

Usage

CreateMarkovCLK(ID, data, password, markovTable, k1 = 20, k2 = 4,
  padding = as.integer(c(0)), qgram = as.integer(c(2)),
  lenBloom = 1000,  includeOriginalBigram = TRUE, v = FALSE)

Arguments

Details

A transition matrix for all possible bigrams is built using a function to fit a markov chain distribution using a Laplacian smoother. A transition matrix built for bigrams using the NC Voter Data is included in the package. For each original bigram in the data, k2 new bigrams are drawn according to their follow-up probability as given by the transition matrix. The final bigram set is then encoded following CreateCLK.

Value

A data.frame containing IDs and the corresponding bit vector.

References

Schnell R., Borgs C. (2017): Using Markov Chains for Hardening Bloom Filter Encryptions against Cryptographic Attacks in Privacy Preserving Record Linkage. German Record Linkage Center Working Paper.

Schnell, R. (2017): Recent Developments in Bloom Filter-based Methods for Privacy-preserving Record Linkage. Curtin Institute for Computation, Curtin University, Perth, 12.9.2017.

See Also

CreateCLK, StandardizeString

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <-read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

## Not run: 
# Load example Markov chain matrix (created from NC Voter Data)
markovFile <-file.path(path.package("PPRL"), "extdata/TestMatrize.csv")
markovData <-read.csv(markovFile,  sep = " ",
  header = TRUE, check.names = FALSE)
markovData <- as.matrix(markovData)

# Create Markov CLK using
CLKMarkov <- CreateMarkovCLK(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  markovTable = markovData,
  k1 = 20, k2 = 4, l = 1000,
  padding = c(0, 0, 1, 1),
  q = c(1, 2, 2, 2),
  password = c("(H]$6Uh*-Z204q", "lkjg", "klh", "KJHklk5"),
  includeOriginalBigram = TRUE)

## End(Not run)

Record Level Bloom Filter (RLBF) Encoding

Description

Creates Record Level Bloom filters, combining single Bloom filters into a singular bit vector.

Usage

CreateRecordLevelBF(ID, data, password, lenRLBF = 1000, k = 20,
                       padding = as.integer(c(0)),
                       qgram = as.integer(c(2)),
                       lenBloom = as.integer(c(500)),
                       method = "StaticUniform",
                       weigths = as.numeric(c(1)))

Arguments

Details

Single Bloom filters are first built for every variable in the input data.frame. Combining the Bloom filters is done by sampling a set fraction of the original Bloom filters and concatenating the samples. The result is then shuffled. The sampling can be done using four different weighting methods:

1. StaticUniform

2. StaticWeighted

3. DynamicUniform

4. DynamicWeighted

Details are described in the original publication.

Value

A data.frame containing IDs and the corresponding bit vector.

Source

Durham, E. A. (2012). A framework for accurate, efficient private record linkage. Dissertation. Vanderbilt University.

See Also

CreateBF, CreateCLK,

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
## Not run: 
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# StaticUniform
RLBF <- CreateRecordLevelBF(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  lenRLBF = 1000, k = 20,
  padding = c(0, 0, 1, 1), qgram = c(1, 1, 2, 2),
  lenBloom = 500,
  password = c("(H]$6Uh*-Z204q", "lkjg", "klh", "KJHkälk5"),
  method = "StaticUniform")

# StaticWeigthed
RLBF <- CreateRecordLevelBF(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  lenRLBF = 1000, k = 20,
  padding = c(0, 0, 1, 1), qgram = c(1, 1, 2, 2),
  lenBloom = 500,
  password = c("(H]$6Uh*-Z204q", "lkjg", "klh", "KJHkälk5"),
  method = "StaticWeigthed", weigths = c(0.1, 0.1, 0.5, 0.3))

# DynamicUniform
RLBF <- CreateRecordLevelBF(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  lenRLBF = 1000, k = 20,
  padding = c(0, 0, 1, 1), qgram = c(1, 1, 2, 2),
  lenBloom = c(300, 400, 550, 500),
  password = c("(H]$6Uh*-Z204q", "lkjg", "klh", "KJHkälk5"),
  method = "DynamicUniform")

# DynamicWeigthed
RLBF <- CreateRecordLevelBF(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  lenRLBF = 1000, k = 20,
  padding = c(0, 0, 1, 1), qgram = c(1, 1, 2, 2),
  lenBloom = c(300, 400, 550, 500),
  password = c("(H]$6Uh*-Z204q", "lkjg", "klh", "KJHkälk5"),
  method = "DynamicWeigthed", weigths = c(0.1, 0.1, 0.5, 0.3))

## End(Not run)

Deterministic Record Linkage

Description

Deterministic Record Linkage of two data sets giving results enabling rule-based methods.

Usage

DeterministicLinkage(IDA, dataA, IDB, dataB, blocking = NULL, similarity)

Arguments

Details

To call the Deterministic Linkage function it is necessary to set up linking variables and methods. Using blocking variables is optional. Further options are available in SelectBlockingFunction and SelectSimilarityFunction.

Value

A data.frame containing ID-pairs and the link status for each linking variable. This way, rules can be put into place allowing the user to classify links and non-links.

Source

Christen, P. (2012): Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

Schnell, R., Bachteler, T., Reiher, J. (2004): A toolbox for record linkage. Austrian Journal of Statistics 33(1-2): 125-133.

See Also

ProbabilisticLinkage, SelectBlockingFunction, SelectSimilarityFunction, StandardizeString

Examples

# load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# define year of birth (V3) as blocking variable
bl <- SelectBlockingFunction("V3", "V3", method = "exact")

# Select first name and last name as linking variables,
# to be linked using the soundex phonetic (first name)
# and exact matching (last name)
l1 <- SelectSimilarityFunction("V7", "V7", method = "Soundex")
l2 <- SelectSimilarityFunction("V8", "V8", method = "exact")

# Link the data as specified in bl and l1/l2
# (in this small example data is linked to itself)
res <- DeterministicLinkage(testData$V1, testData,
  testData$V1, testData, similarity = c(l1, l2), blocking = bl)

Elegant Pairing

Description

Unordered Pairing Function creating a new unique integer from two input integers.

Usage

ElegantPairingInt(int1, int2)

Arguments

Details

With two of non-negative integers x and y as an input, the pairing is computed as:

elegantPairing(x, y) = (x * y) + floor ((|x - y| - 1)^2) / 4)

The function is commutative. x and y have to be non-negative integers.

Value

The function outputs a single non-negative integer that is uniquely associated with that unordered pair.

Source

Szudzik, M. (2006): An Elegant Pairing Function. Wolfram Science Conference NKS 2006.

See Also

ElegantPairingVec

Examples

ElegantPairingInt(2, 3)

Simple Pairing Function

Description

Unordered Pairing Function creating a new unique integer from two input integers in a data.frame.

Usage

ElegantPairingVec (ID, data)

Arguments

Details

With two of non-negative integers x and y as an input, the pairing is computed as:

elegantPairing(x, y) = (x * y) + floor ((|x - y| - 1)^2) / 4)

The function is commutative. x and y have to be non-negative integers. The function outputs a single non-negative integer that is uniquely associated with that unordered pair.

Value

A data.frame containing IDs and the computed integer.

Source

Szudzik, M. (2006): An Elegant Pairing Function. Wolfram Science Conference NKS 2006.

See Also

ElegantPairingInt

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create numeric data frame of day and month of birth
dataInt <- data.frame(as.integer(testData$V4), as.integer(testData$V5))

# Use unordered pairing on day and month
res <- ElegantPairingVec(testData$V1, dataInt)

Probabilistic Record Linkage

Description

Probabilistic Record Linkage of two data sets using distance-based or probabilistic methods.

Usage

ProbabilisticLinkage(IDA, dataA, IDB, dataB,  blocking = NULL, similarity)

Arguments

Details

To call the Probabilistic Linkage function it is necessary to set up linking variables and methods. Using blocking variables is optional. Further options are available in SelectBlockingFunction and SelectSimilarityFunction. Using this method, the Fellegi-Sunter model is used, with the EM algorithm estimating the weights (Winkler 1988).

Value

A data.frame containing pairs of IDs, their corresponding similarity value and the match status as determined by the linkage procedure.

Source

Christen, P. (2012): Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

Schnell, R., Bachteler, T., Reiher, J. (2004): A toolbox for record linkage. Austrian Journal of Statistics 33(1-2): 125-133.

Winkler, W. E. (1988): Using the EM algorithm for weight computation in the Fellegi-Sunter model of record linkage. Proceedings of the Section on Survey Research Methods Vol. 667, American Statistical Association: 671.

See Also

CreateBF, CreateCLK, DeterministicLinkage, SelectBlockingFunction, SelectSimilarityFunction, StandardizeString

Examples

# load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# define year of birth (V3) as blocking variable
bl <- SelectBlockingFunction("V3", "V3", method = "exact")

# Select first name and last name as linking variables,
# to be linked using the Jaro-Winkler similarity measure (first name)
# and levenshtein distance (last name)
l1 <- SelectSimilarityFunction("V7", "V7", method = "jw")
l2 <- SelectSimilarityFunction("V8", "V8", method = "lv")

# Link the data as specified in bl and l1/l2
# (in this small example data is linked to itself)
res <- ProbabilisticLinkage(testData$V1, testData,
  testData$V1, testData, similarity = c(l1, l2), blocking = bl)

Select blocking method prior to linkage

Description

Before calling ProbabilisticLinkage or DeterministicLinkage, a blocking method can be selected. For each blocking variable desired, the function call has to be repeated.

Usage

SelectBlockingFunction(variable1, variable2, method)

Arguments

Details

The following methods are available for blocking:

'exact'

Simple exact blocking. All records with the same values for the blocking variable create a block. Searching for links is only done within these blocks.

'exactCL'

The same as 'exact'. Only works with strings; all caracters are capitalised.

References

Christen, P. (2012): Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

See Also

DeterministicLinkage, ProbabilisticLinkage, SelectSimilarityFunction, StandardizeString

Examples

# load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# define year of birth (V3) as blocking variable
bl <- SelectBlockingFunction("V3", "V3", method = "exact")

# Select first name and last name as linking variables,
# to be linked using the Jaro-Winkler similarity measure (first name)
# and levenshtein distance (last name)
l1 <- SelectSimilarityFunction("V7", "V7", method = "jw")
l2 <- SelectSimilarityFunction("V8", "V8", method = "lv")

# Link the data as specified in bl and l1/l2
# (in this small example data is linked to itself)
res <- ProbabilisticLinkage(testData$V1, testData,
  testData$V1, testData, similarity = c(l1, l2), blocking = bl)

Select Similarity Function for Linkage

Description

To call DeterministicLinkage or ProbabilisticLinkage it is mandatory to select a similarity function for each variable. Each element of the setup contains the two variable names and the method. For some methods further informations can be entered.

Usage

SelectSimilarityFunction(variable1, variable2,
  method = "jw",
  ind_c0 = FALSE, ind_c1 = FALSE,
  m = 0.9, u = 0.1, p = 0.05, epsilon = 0.0004,
  lower = 0.0, upper = 0.0,
  threshold = 0.85, jaroWeightFactor = 1.0, lenNgram = 2)

Arguments

Value

Calling the function will not return anything.

References

Christen, P. (2012): Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

Schnell, R., Bachteler, T., Reiher, J. (2004): A toolbox for record linkage. Austrian Journal of Statistics 33(1-2): 125-133.

Winkler, W. E. (1988): Using the EM algorithm for weight computation in the Fellegi-Sunter model of record linkage. Proceedings of the Section on Survey Research Methods Vol. 667, American Statistical Association: 671.

See Also

DeterministicLinkage, ProbabilisticLinkage, SelectBlockingFunction, StandardizeString

Examples

# load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# define year of birth (V3) as blocking variable
bl <- SelectBlockingFunction("V3", "V3", method = "exact")

# Select first name and last name as linking variables,
# to be linked using the jaro-winkler (first name)
# and exact matching (last name)
l1 <- SelectSimilarityFunction("V7","V7", method = "jw",
  ind_c0 = FALSE, ind_c1 = FALSE , m = 0.9, u = 0.1,
  lower = 0.0, upper = 0.0)
l2 <- SelectSimilarityFunction("V8","V8", method = "exact")

# Link the data as specified in bl and l1/l2
# (in this small example data is linked to itself)
res <- ProbabilisticLinkage(testData$V1, testData,
  testData$V1, testData, similarity = c(l1, l2), blocking = bl)

Standardize String

Description

Preprocessing (cleaning) of strings prior to linkage.

Usage

StandardizeString(strings)

Arguments

Details

Strings are capitalized, letters are substituted as described below. Leading and trailing blanks are removed. Other non-ASCII characters are deleted.

• Replace "Æ" with "AE"

• Replace "æ" with "AE"

• Replace "Ä" with "AE"

• Replace "ä" with "AE"

• Replace "Å" with "A"

• Replace "å" with "A"

• Replace "Â" with "A"

• Replace "â" with "A"

• Replace "À" with "A"

• Replace "à" with "A"

• Replace "Á" with "A"

• Replace "á" with "A"

• Replace "Ç" with "C"

• Replace "Ç" with "C"

• Replace "Ê" with "E"

• Replace "ê" with "E"

• Replace "È" with "E"

• Replace "è" with "E"

• Replace "É" with "E"

• Replace "é" with "E"

• Replace "Ï" with "I"

• Replace "ï" with "I"

• Replace "Î" with "I"

• Replace "î" with "I"

• Replace "Ì" with "I"

• Replace "ì" with "I"

• Replace "Í" with "I"

• Replace "í" with "I"

• Replace "Ö" with "OE"

• Replace "ö" with "OE"

• Replace "Ø" with "O"

• Replace "ø" with "O"

• Replace "Ô" with "O"

• Replace "ô" with "O"

• Replace "Ò" with "O"

• Replace "ò" with "O"

• Replace "Ó" with "O"

• Replace "ó" with "O"

• Replace "ß" with "SS"

• Replace "Ş" with "S"

• Replace "ş" with "S"

• Replace "ü" with "UE"

• Replace "Ü" with "UE"

• Replace "Ů" with "U"

• Replace "Û" with "U"

• Replace "û" with "U"

• Replace "Ù" with "U"

• Replace "ù" with "U"

Value

Returns a character vector with standardized strings.

Examples

strings = c("Päter", " Jürgen", " Roß")
StandardizeString(strings)

Apply Wolframs rule 30 on bit vectors

Description

Apply Wolframs Cellular Automaton rule 30 on the input bit vectors.

Usage

WolframRule30(ID, data, lenBloom, t)

Arguments

Value

Returns a character vector with new bit vectors after rule 30 has been applied t times.

References

https://en.wikipedia.org/wiki/Rule_30

Schnell, R. (2017): Recent Developments in Bloom Filter-based Methods for Privacy-preserving Record Linkage. Curtin Institute for Computation, Curtin University, Perth, 12.9.2017.

Wolfram, S. (1983): Statistical mechanics of cellular automata. Rev. Mod. Phys. 55 (3): 601–644.

See Also

WolframRule90

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create bit vector e.g. by CreateCLK or CreateBF
CLK <- CreateCLK(ID = testData$V1,
  data = testData[, c(2, 3, 7, 8)],
  k = 20, padding = c(0, 0, 1, 1),
  q = c(1, 1, 2, 2), l = 1000,
  password = c("HUh4q", "lkjg", "klh", "Klk5"))

# Apply rule 30 once
res <- WolframRule30(CLK$ID, CLK$CLK, lenBloom = 1000, t = 1)

Apply Wolframs rule 90 on bit vectors

Description

Apply Wolframs Cellular Automaton rule 90 on the input bit vectors.

Usage

WolframRule90(ID, data,  lenBloom, t)

Arguments

Value

Returns a character vector with new bit vectors after rule 90 has been applied t times.

References

https://en.wikipedia.org/wiki/Rule_90

Martin, O., Odlyzko, A. M., Wolfram, S. (1984): Algebraic properties of cellular automata. Communications in Mathematical Physics, 93 (2): 219-258.

Schnell, R. (2017): Recent Developments in Bloom Filter-based Methods for Privacy-preserving Record Linkage. Curtin Institute for Computation, Curtin University, Perth, 12.9.2017.

Wolfram, S. (1983): Statistical mechanics of cellular automata. Rev. Mod. Phys. 55 (3): 601–644.

See Also

WolframRule30

Examples

# Load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
  colClasses = "character")

# Create bit vector e.g. by CreateCLK or CreateBF
CLK <- CreateCLK(ID = testData$V1, 
  data = testData[, c(2, 3, 7, 8)],
  k = 20, padding = c(0, 0, 1, 1), 
  q = c(1, 1, 2, 2), l = 1000, 
  password = c("HUh4q", "lkjg", "klh", "Klk5"))

# Apply rule 90 once  
res <- WolframRule90(CLK$ID, CLK$CLK, lenBloom = 1000, t = 1)