PPRL (version 0.3.8)

CreateBalancedBF: 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)

Value

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

Arguments

ID

A character vector or integer vector containing the IDs of the data.frame.

data

Bit vectors as created by any Bloom filter-based method.

password

a string used as a password for the random permutation.

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")