Skip to contents

datablinder

“I have this data. I want an analysis and an output. I want an AI to write the code. But the data is sensitive or proprietary, so I can’t drop it into a chat.”

datablinder turns a data file into a blinded copy: an anonymised, synthetic look-alike with the same columns, the same R classes, the same file format, values that look roughly similar, and not one real value in it. Share the copy with a collaborator or an AI assistant, get code back, run that code on the real file.

There is one thing it has to get right, and everything in the package is built around it: code written against the blinded copy runs unchanged on the real data. Integers stay integers, a 0/1 column stays 0/1, dates keep their format, a factor keeps its number of levels, and no column ever changes class.

The datablinder app: the options in the sidebar, the summary of the blinded data and a preview of its first rows in the main area
The datablinder app: the options in the sidebar, the summary of the blinded data and a preview of its first rows in the main area

Install 

# install.packages("remotes")
remotes::install_github("agallinat/datablinder")

Not on CRAN yet.

A quick look 

library(datablinder)
blind_file("patients.csv")
#> Blinded copy written to patients_blinded.csv
#>
#>   patient_id  character  new IDs, same shape (A-000000)
#>   visit_date  character  synthetic dates, format %d/%m/%Y
#>   age         integer    numeric, synthetic values
#>   sex         character  2 categories -> A, B
#>   region      character  3 categories -> A, B, C
#>   treated     integer    discrete numbers, same values
#>   weight_kg   numeric    numeric, synthetic values
#>   email       character  fake addresses at example.com
#> Rows: 40
#> Leak check: passed

patients_blinded.csv is a comma-separated file with those eight columns, in that order, forty rows long, missing values written the way the input wrote them:

patient_id,visit_date,age,sex,region,treated,weight_kg,email
M-794949,18/04/2023,57,B,A,0,78.7,user_zp98@example.com
M-889029,18/09/2023,33,A,C,1,80.7,user_wd06@example.com
F-753189,05/07/2023,27,B,A,0,61,user_kw07@example.com

That summary is the other half of the point. It describes the blinded data only, so it is safe to paste into a chat along with the file, and it tells an AI what each column is before it writes a line of code.

Three ways to use it

In R 

blind_file("patients.xlsx")                       # -> patients_blinded.xlsx
blind_file("data.sav", output = "shared/fake.sav", blind_names = TRUE,
           rows = 200, seed = 1)

fake <- blind_data(my_data_frame)                 # data frame in, data frame out
attr(fake, "blind_summary")                       # the same summary

blind_file() is blind_data() with a reader and a writer around it. Both return the summary; blind_data() hangs it on the result as an attribute.

In a browser

A small app on 127.0.0.1, so the file never leaves the computer. Choose a file, set the options, press Blind, read the summary, copy it with one button, look at the first ten blinded rows, download the copy. In RStudio it is also under Addins > Blind a data file. It is the app in the screenshot above.

From a terminal

Nothing to install beyond the package itself:

Rscript -e 'datablinder::blind_file("patients.csv")'

If you do this often, install_cli() copies a small wrapper script into a directory on your PATH (and writes the .cmd shim Windows needs). It shows you the directory and asks before writing anything, never uses sudo and never edits a shell configuration file; it then tells you plainly whether the shell will find the command, and what to add to your PATH if it will not.

datablinder patients.csv --blind-names --rows 200 --seed 42
datablinder trial.csv --keep-real arm,visit
datablinder --help

The exit status is non-zero if the file cannot be read or written, or if the leak check fails.

The five options

Argument Default What it does
blind_names FALSE Rename the columns to col_01, col_02… and drop SPSS/Stata variable labels, which often describe sensitive content
keep_labels FALSE FALSE: category values and factor levels become A, B, C… TRUE: keep the real ones, for when code must filter on them (region == "North")
keep_real NULL Names of columns to copy over unblinded, with their real values. For a column that carries nothing sensitive and that code has to use as it is, such as a treatment arm or a study visit. Cannot be combined with rows. Read the note below
rows NULL How many rows to generate. NULL means as many as the input
seed NULL A seed, for a copy that can be reproduced. Your session’s .Random.seed is put back as it was found

That is the whole interface. Detection thresholds are internal constants, and there is no configuration file.

keep_real, and what it costs

Sometimes code cannot be written without a real value: arm == "placebo", group_by(site). keep_real names the columns to leave alone.

blind_file("trial.csv", keep_real = c("arm", "visit"))

Those two columns come out exactly as they went in, still lined up with their own rows, and keeping their real names even under blind_names = TRUE. Everything else is blinded as usual.

It is the one way to make this package disclose real data, so it is worth a moment’s thought:

  • A column that is harmless by itself can still identify someone in combination with the others. A real postcode, date of birth or site, next to a real sex and a real visit date, can be enough even with every name blinded.
  • The leak check cannot vouch for a kept column and does not pretend to. It reports them instead, so the summary reads Leak check: passed, except 2 columns kept real: arm, visit, and each kept column’s own line reads REAL VALUES KEPT, not blinded.
  • rows cannot be used at the same time, because a kept column stays matched to the rest of its row.

If a column is only needed for its categories and not its contents, keep_labels = TRUE is usually enough and keeps no values.

Files it reads and writes

Format Notes
CSV / TSV (.csv, .txt, .tsv, .tab) Delimiter, decimal mark, encoding (UTF-8 or Latin-1), byte order mark, line endings and missing-value text all come back the way they went in
Excel (.xlsx, .xlsm, .xls) Every sheet is blinded, sheet names kept. Written as .xlsx, the only Excel format that can be written
SPSS (.sav, .zsav), Stata (.dta) Value labels, variable labels and display formats kept
R (.rds) Must hold a data frame; its class (data.frame, tibble, data.table) is kept
Parquet (.parquet) Needs the suggested arrow package

The copy goes next to the input as <name>_blinded.<ext> unless output says otherwise.

What happens to each column

Types are decided by values, not only by storage class: a double holding nothing but 0 and 1 is binary, and a double holding whole numbers with few distinct values is discrete. That is what keeps table(x), x == 1 and factor(x) behaving the same way on both files.

Detected as Blinded as
Continuous numeric Drawn from a smoothed inverse CDF of the real column, so the shape survives; slightly shifted minimum and maximum, the same number of decimals, the same sign, the same share of exact zeros, the same class
Discrete numeric (whole numbers, few distinct values, including 0/1) The same set of values in similar proportions
Factor, ordered factor, character with few distinct values, haven_labelled The same number of levels or categories in similar proportions, relabelled A, B, C… unless keep_labels. Order, unused levels and haven codes kept
Logical A similar share of TRUE
Date, POSIXct Dates in a slightly shifted range, same timezone. Dates that were text in the file stay text, in the same format
Identifier (nearly all values distinct, or a name like patient_id) New unique values of the same shape (P-004213 -> M-794949); repeated IDs repeat a similar number of times
Email, phone, URL Obviously fake values of the same shape, at example.com
Fixed-shape text (postcodes, codes with leading zeros) Random letters, digits and punctuation in the same positions
Free text Placeholder words of similar length
Constant, all-NA All-NA stays; a constant number stays; constant text is replaced

Every column keeps its share of missing values. Columns are blinded independently: relationships between them are deliberately not preserved.

The leak check

Every run is checked before anything is written, and the result is in the summary.

Character, factor, label and identifier columns are built from the shape of the real values rather than the values themselves, so any overlap with the real column is a bug: it is a hard failure, and nothing is written. Relabelled categories are checked for having actually been relabelled.

Exact matches in continuous numeric, date and free-text columns are counted and reported, not forbidden. They carry no information: a column of ages rounded to whole years has only so many values to go round, date ranges are shifted rather than moved, and placeholder words come from a fixed list. A count here is expected and is not a leak of anything an AI could use:

Leak check: passed
Exact numeric or date coincidences: 33 (expected with rounded values and shifted date ranges)

Limits

Read this part.

  • This is not a formal privacy guarantee. It reduces the risk of disclosing the real values. The column names, the classes, the row count, the share of missing values, the number of categories and the broad shape of every distribution all stay visible, on purpose, because that is what makes the code work. If the structure itself is confidential, use blind_names = TRUE and think again about sharing the file at all.
  • Read the summary before you share the file. It is the list of decisions the package made. A column blinded as a category when it is really an identifier, or as free text when it is really a code, is the case to catch, and the summary is where you see it.
  • keep_labels = TRUE keeps real category labels. Only use it when those labels are not themselves sensitive.
  • keep_real keeps whole columns. Every value in a column you name is shared as it is. Use it for the columns that carry nothing sensitive and that the code cannot be written without, check them against the rest of the table before you share the file, and leave it alone otherwise.
  • The blinded data is for writing code, not for analysis. Since columns are blinded independently, no correlation, model coefficient, group difference or cross-tabulation on the copy means anything about the real data. Results will be structurally similar and numerically meaningless.
  • Code that depends on the content of free text will not work. Free text becomes placeholder words. Neither will code that relies on relationships between columns, or on a particular real value existing.
  • Detection needs something to look at. On a table of five rows there is little to tell an identifier from a measurement; check the summary, and use rows to make the copy longer if the code you get back needs room.
  • No network access, and no AI or LLM calls, anywhere in the package. The reading, blinding and writing all happen on your machine. Sharing the blinded file with an AI is a thing you do afterwards, deliberately.

Not in this version: preserving relationships between columns, per-column overrides, reading from a database, and complex objects such as Seurat or SingleCellExperiment.

Documentation

Full documentation is at agallinat.github.io/datablinder, including three longer articles:

Credit

FakeDataR (MIT) came first and has a similar goal. Three of its ideas are used here: recognising sensitive columns by matching their names against a word list, printing a short description of the fake data to paste into a chat, and validating the copy against the original by comparing classes and missingness. Parts of the name word lists in R/detect.R are adapted from it.

datablinder differs in what it refuses to do. FakeDataR draws numeric columns uniformly between the real minimum and maximum or resamples the real values, turns 0/1 doubles into decimals, keeps real category labels by default, returns 30 rows instead of the input’s row count, and changes types by default (percent strings to numbers, yes/no text to a factor, date text to POSIXct). Each of those breaks code written against the copy when it meets the real data, and each has a test here proving datablinder does not do it.

License

MIT. See LICENSE.md.