SDC in R: ProtectTable() does not converge

Question

I have a dataset that contains 4 categorical variables, with two of them with a cardinality of 4, one of 2 and one of 3. I want to apply ProtectTable() from the R Package SDC. To include the possibility of multiple values being true within one variable, I one hot encoded the variables into a matrix of binary variables for each possible value of a variable, thus resulting in 4 + 4 + 2 + 3 variables, each with a cardinality of 2. Using this many variables does not converge unfortunately. Do you guys have an idea on how to approach this so that my solution converges or alternatively another approach to ensure the possibility of multiple true values? I tried creating a joined variable, that contains every possible combination of the variable. But then the disclosure control was not successful.

Id be happy for any help!

Kindly, StringDetector

I tried using the ProtectTable() command after defining the dimensions. The command worked fine with the non-encoded variables.

onehotencoded <- df_ref %>%
  mutate(
    FQuarter_1 = if_else(FQuarter == "Q1", 1, 0),
    FQuarter_2 = if_else(FQuarter == "Q2", 1, 0),
    FQuarter_3 = if_else(FQuarter == "Q3", 1, 0),
    FQuarter_4 = if_else(FQuarter == "Q4", 1, 0),
    FYear_24 = if_else(FYear == "2024", 1, 0),
    FYear_23 = if_else(FYear == "2023", 1, 0),
    FYear_22 = if_else(FYear == "2022", 1, 0),
    FYear_21 = if_else(FYear == "2021", 1, 0),
    Working_model_FT = if_else(Working_model == "Full-time", 1, 0),
    Working_model_PT = if_else(Working_model == "Part-time", 1, 0),
    Gender_Male = if_else(Gender == "men", 1, 0),
    Gender_Female = if_else(Gender == "women", 1, 0),
    Gender_Diverse = if_else(Gender == "else", 1, 0)
  ) %>%
  select(-FQuarter, -FYear, -Working_model, -Gender)


df_ref <- onehotencoded


# Negative Threshold ------------------------------------------------------

negative_threshold_function <- function(df_ref, threshold) {
  # convert inputs to character type
  df_ref <- df_ref %>% mutate_all(as.character)
  
  # Create dimList object dynamically for all dimensions in df_ref (except the first column org_level_hierachy)
  dimList <- list()
  
  for (col_name in colnames(df_ref)) {
    dim_hierarchy <- sdcHierarchies::hier_create(root = "Total", nodes = unique(df_ref[[col_name]]))
    dim_data <- matrix(nrow = nrow(dim_hierarchy), ncol = 2)
    dim_data[,1] <- sapply(dim_hierarchy$level, function(x) paste(replicate(x, "@"), collapse = ""))
    dim_data[,2] <- dim_hierarchy$leaf
    colnames(dim_data) <- c("levels", "codes")
    dim_data <- as.data.frame(dim_data)
    dimList[[col_name]] <- dim_data
  }
  
  # Protect table, with secondary suppression
  df_aggregated_protected <- ProtectTable(df_ref,
                                          dimList = dimList,
                                          infoAsFrame = T,
                                          #method = "HITAS",
                                          method = "SIMPLEHEURISTIC_OLD",
                                          maxN = threshold,
                                          maxVars = 2,
                                          #maxVars = ncol(df_ref) - 1,
                                          fastSolution = TRUE,
                                          save = FALSE, # save tables in between
                                          timeLimit = 0.1, #so to say a timeout, set very low, trying to fasten computation
                                          verbose = TRUE) #"Total" means filter is not selected.
  
  #small adjustments: sdcStatusLegend, Suppression column
  df_aggregated_protectedFinal <- df_aggregated_protected[["data"]] %>% 
    mutate(sdcStatusLegend = case_when(
      sdcStatus == "u" ~ "cell is primary suppressed and needs to be protected",
      sdcStatus == "x" ~ "cell has been secondary suppressed",
      sdcStatus == "s" ~ "cell can be published",
      sdcStatus == "z" ~ "cell must not be suppressed"
    )) %>% 
    mutate(suppressed = case_when(
      is.na(suppressed) ~ "yes",
      !is.na(suppressed) ~ NA_character_
    )) %>% 
    rename(group_size = freq, suppress = suppressed) %>% 
    relocate(sdcStatus, .after = suppress)
  
  return(df_aggregated_protectedFinal)
}

runtime <- system.time({
  final_table <- negative_threshold_function(df_ref, threshold = 5)
})