WolframRule30: Apply Wolframs rule 30 on bit vectors

In PPRL: Privacy Preserving Record Linkage

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

ID	IDs as character vector.
data	character vector containing bit vectors.
lenBloom	length of Bloom filters.
t	indicates how often rule 30 is to be used.

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)

PPRL documentation built on Nov. 10, 2022, 5:41 p.m.