Anonymize data

Data can be anonymized using scripts with the following methods:

• When data is extracted from a source system, the data is immediately anonymized and the anonymized data is stored to datatables.
• Data in the datatables are analymized and stored to other datatables, where the anonymized data can be exported or visualized in dashboards.

If the original data is stored in the system, make sure that users who are only allowed to see the anonymized data, don't have access to the original data.

Following example script anonymizes selected columns in a datatable and writes the result to a new datatable. Each anonymized data value gets an numeric value starting from one (this can also be called pseudonymization).

let anonymizationDict = #{};
function AnonymizeColumn(columnName, oldValue) {
  let dict
  if (!anonymizationDict.ContainsKey(columnName)) {
    dict = #{};
    anonymizationDict.Set(columnName, dict);
  }
  else {
    dict = anonymizationDict[columnName];
  }
  if (!dict.ContainsKey(oldValue)) {
    dict.Set(oldValue, `${dict.Count + 1}`);
  }
  return dict[oldValue];
}

function Anonymize(df, cols) {
  for (let i = 0; i < CountTop(cols); ++i) {
    let col = cols[i];
    df = df.SetColumns([
      `${col}`: () => AnonymizeColumn(col, Column(col))
    ]);
  }
}
let sourceDatatable = DataTableById(1);
Anonymize(sourceDatatable.SqlDataFrame.Collect(), ["Case Name","Customer Business Team","Customer Name","Customer Number"])
.Persist(sourceDatatable.Name + "_anonymized", #{"ProjectId": sourceDatatable.Project.Id})