This is the reference implementation of SynDiffix. It is two primary roles:

• To test the properties and performance of SynDiffix.
• To serve as the core description of the technology from which other implementations and integrations can be built.

From inside the src\SynDiffix folder execute:

dotnet run file.csv --columns column0:r column1:i ...

<file.csv> - Specifies the input CSV file.

--columns [<string>...] - The selection of columns to synthesize and their types in the format name:x, where x is one of the following: b-boolean, i-integer, r-real, t-timestamp, s-string.

The Python wrapper syndiffix.py uses scikit-learn to find the most relevant ML features for a target column.

python syndiffix.py <input-path> <output-path> --dev --ml_target=<ml-target-column>

Run syndiffix.py --help for usage guide.

--output <string>, -o <string> - Path for CSV output. If not specified, result is printed to stdout.

--aidcolumns [<string>...] - List of entity ID columns used for anonymization. If not specified, assumes each row represents a distinct protected entity.

--verbose - Display extra output for debugging purposes.

--lcf-low-threshold, -lcf <int> - Low threshold for the low-count filter.

--range-low-threshold, -range <int> - Low threshold for a range bucket.

--singularity-low-threshold, -sing <int> - Low threshold for a singularity bucket.

--threshold-sd, -thresh_sd <double> - Threshold SD for LCF/range/singularity decisions.

--outlier-count <int> <int> - Outlier count interval.

--top-count <int> <int> - Top values count interval.

--layer-noise-sd, -noise_sd <double> - Count SD for each noise layer.

--no-clustering - Disables column clustering.

--clustering-maincolumn <string> - Column to be prioritized in clusters for better ML accuracy.

--clustering-mainfeatures [<string>...] - Best features of main column. When specified, this overrides the default clustering algorithm.

--clustering-samplesize <int> - Table sample size when measuring dependence.

--clustering-maxweight <double> - Maximum cluster weight. Default value of 15.0 is approximately 4-7 columns, depending on their variance.

--clustering-thresh-merge <double> - Dependence threshold for combining columns in a cluster.

--precision-limit-row-fraction <int> - Tree nodes are allowed to split if node_num_rows >= table_num_rows/row_fraction.

--precision-limit-depth-threshold <int> - Tree depth threshold below which the row-fraction check is not applied.

The fewer columns that are included with the --columns parameter, the more accurate the resulting synthetic data. If for instance one is only interested in column pairs, then it is better to generate a different synthetic dataset for each pair than to take the pairs from one complete synthetic dataset.

If the synthetic dataset is for the purpose of building an ML model, and the target column of the model is known, then setting --clustering-maincolumn to the target column will produce synthetic data that leads to a better ML model.

Running syndiffix through the Python wrapper will automatically find the best ML features for the target column. This results in better ML quality compared to the univariate measures that syndiffix uses internally for feature detection.

If there is more than one row per protected entity (i.e. per individual), then the --aidcolumns parameter must be set.

All other parameters should normally be kept at their defaults.

All of the following steps, whenever a count is mentioned, refer to the anonymized (noisy) count (the true count is never used directly).

Before the forest building is conducted, preliminary casting of all values to reals is done:

• null - see Forest building
• boolean - false becomes 0.0, true becomes 1.0
• integer - natural cast to real
• real - kept intact (TODO: document behavior for non-finite reals, once it's implemented)
• string - a sorted array with all of the distinct values is first created, then each value gets assigned its position in the array
• timestamp - becomes seconds since 1800-01-01 00:00:00 UTC

There are two types of nodes: branches and leaves. Both kinds store an array of ranges, an array of subnodes, and a stub flag. A branch has 2^tree_dimensions children, some of which can be missing. A leaf stores the list of rows matching its ranges.

• For each row:
  • For each column:
    • Compute the range of the values space.
• For each column:
  • Map nulls to a value outside twice the actual min or max boundary, to prevent leaking low-count nulls into the bottom or top values range.
  • Snap the range size to the next power-of-two.
  • Align the range boundaries to the snapped size.
• For k in 1..length(columns):
  • For each combination of k columns:
    • Build the tree root for the column combination.
    • For each row:
      • Find target leaf node. If one doesn't exist:
        • Create a new leaf node by halfing the ranges of the parent.
        • Fill the subnodes list from the lower dimensional trees.
          • The subnodes are the children of parent's subnodes with the same corresponding ranges as the current node.
        • Determine if the node is a stub.
          • A node is a stub if all of its subnodes either:
            • are missing;
            • fail the range / singularity threshold;
            • are also stubs themselves.
      • Add the row to its matching leaf node.
      • If target node is not a singularity, passes LCF, is not a stub, and (below depth threshold or over row fraction):
        • Convert the target leaf node to a branch node.
        • Add the previously stored rows to the newly created node.
    • If k = 1:
      • Push the root down as long as one of its children fails LCF.
      • Add the rows from the previously dropped low-count nodes to the remaining tree.
      • Update the snapped range for the corresponding dimension.

A bucket has an array of ranges (one for each dimension), and a noisy row count. When creating a bucket from a node, use the node's singularity ranges at its singularity dimensions and its snapped ranges elsewhere.

• Do a bottom-up traversal of the forest's root. At each node:
  • If the node is a leaf:
    • If the node fails LCF:
      • Return nothing.
    • If the node is a singularity:
      • Return a bucket from node's single value.
    • If in 1-dimensional node:
      • Return a bucket from node's ranges.
    • Else:
      • Return the refined buckets from node's ranges and subnodes.
  • Else:
    • Gather all buckets from child nodes.
    • If there are fewer rows in the children's buckets than half of the current node's rows:
      • If in 1-dimensional node:
        • Return a bucket from node's ranges.
      • Else:
        • Generate current.count - children.count refined buckets from node's ranges and subnodes.
        • Append the refined buckets to the previously harvested buckets.
        • Return the resulting buckets.
    • Else:
      • Adjust the counts of the buckets so that their sum matches the count of the current node.
      • Return the resulting buckets.

• Gather the sub-dimensional buckets. For each possible subnode:
  • If the subnode's combination corresponds to a singularity:
    • Return a bucket with singularity ranges for it.
  • If the subnode exists:
    • Harvest and return the buckets for it.
  • Else:
    • Return an empty list.
• If insufficient buckets for a subnode:
  • Return the input bucket.
• Compute the possible cumulative ranges for each subnode and dimension.
• Compute the smallest ranges for all dimensions.
• Compact smallest ranges by excluding any low-count half-ranges.
• Gather the possible per-dimension ranges which are inside the smallest ranges.
• Drop the sub-dimensional buckets which are outside the smallest ranges.
• If insufficient ranges:
  • Return the input bucket.
• Else:
  • Generate new buckets by matching the remaining possible ranges of a column with their corresponding sub-dimensional buckets using a deterministic random shuffle.

• For each harvested bucket:
  • For 1..bucket.count:
    • For each range in the bucket:
      • Generate a random float value inside the range.
      • Cast the generated value back to the corresponding column type.
        • In the case of string columns:
          • If the range is a singularity:
            • Return the exact value.
          • Else:
            • Return the common prefix of the range, plus *, plus a random integer from inside the range.
    • Return a synthetic row from the generated values.

When input data has a large number of dimensions, columns are divided into clusters. The solver searches for permutations that build clusters with best overall dependence between columns.

• Current permutation <- {1..dimensions}.
• Best solution <- quality of initial permutation.
• Temperature <- initial temperature.
• While temperature > minimum temperature:
  • New permutation <- swap a random pair in current permutation.
  • If quality of new permutation is better than quality of current permutation,
  • Or accept with some probability depending on temperature:
    • Current permutation <- new permutation.
  • If quality of new permutation better than quality of best permutation:
    • Best solution <- current permutation.
  • Lower temperature based on a curve function.
• Return clusters built from best solution.

Each cluster has a maximum (configurable) weight. A column's weight is computed as 1 + sqrt(max(1, entropy)).

• Read columns in the permutation from left to right.
• Keep adding columns to the initial cluster until:
  • The initial cluster is full,
  • Or the next column has average dependence score (to the cluster's columns) below merge threshold.
• Rest of columns are added to derived clusters. Initially derived clusters are an empty list.
• For each remaining column (left to right):
  • Find all derived clusters where:
    • Average dependence score is above merge threshold.
    • Cluster's current weight + this column's weight do not exceed 70% of max cluster weight.
  • Pick cluster with best average score.
  • If such cluster exists:
    • Add column to cluster.
  • Else:
    • Start a new cluster with this column.
• For all derived clusters (earliest to latest):
  • Look back at the columns that are available so far (including initial cluster).
  • Sort available columns by average dependence score to the derived columns (best to worst).
  • Always use first column from this list as a stitch column.
  • For each remaining column:
    • If cluster has available space and column's average dependence score is above threshold:
      • Add stitch column to cluster.
• Return initial cluster and derived clusters.

If we have a main column, it will always appear in the initial cluster, and always as the first stitch column in derived clusters.

Clustering quality is the average unsatisfied dependence between pairs of columns. A lower value is better because more dependencies are preserved.

• Create a copy of the dependence matrix.
• For each cluster in clusters:
  • For each possible pair col1, col2 in cluster:
    • Set matrix's cells (col1,col2) to zero and (col2,col1) to zero.
• Take the sum of the matrix, excluding values in the diagonal.
• Return sum / (2 * num_columns).

Dependence between columns is a univariate measure that returns a score from 0.0 to 1.0. SynDiffix uses a modified version of chi2 dependence measure. The dependence matrix is the num_columns x num_columns matrix with all pairs of scores.

Stitching is the process of combining microdata from two or more clusters into a single table.

• Initial microtable <- synthesize initial cluster.
• For each cluster in derived clusters:
  • Produce synthetic data for cluster.
  • microtable <- stitch current microtable with cluster's microdata.
• Return the complete microtable.

By "left rows" we refer to the current microtable, and "right rows" refers to the upcoming cluster's microtable.

If the cluster has no stitch columns, we perform a patch:

• Randomly shuffle right rows.
• Align the number of right rows to be that of left rows.
  • If both sides have equal count of rows, do nothing.
  • If right has fewer rows, repeat left.count - right.count random rows from the right side.
  • If right has more, drop right.count - left.count last rows from the right side.
• Zip left and right rows:
  • For each pair, produce a microdata row that combines columns of both sides.
• Return list of combined rows.

For clusters with stitch columns, we perform a recursive stitch.

• Preparation step
  • Sort stitch (common) rows from lowest entropy to highest.
  • For each stitch column, start from the complete 1-dim range of that column.
  • Start the sorting process from the first stitch column in the sorted list.
• Sort both left and right sides by the current sort column.
• Find the midpoint of sort column's 1-dim range.
• Split both left and right rows based on the midpoint.
  • We call rows that have a value of greater than or equal to the midpoint the "upper half".
  • Rows that have value less than the midpoint are called the "lower half"
• If the lower halves of opposing microtables have a roughly equal number of rows, and same for upper halves:
  • Accept the split.
  • Move to the next sort column in the list. Wrap around if necessary.
  • Recursively stitch lower left with lower right. The nested stitch sees only the lower half of the 1-dim range.
  • Recursively stitch upper left with upper right. The nested stitch sees only the upper half of the 1-dim range.
• Else:
  • Reject the split.
  • Move to the next sort column in the list. Wrap around if necessary.
  • If all sort columns were rejected consecutively:
    • Merge left and right.
    • Emit merged rows.
  • Else:
    • Recursively stitch left and right rows.

• Randomly shuffle left and right rows.
• Compute the average length of rows between left and right side.
• Align left and right rows to that length.
  • If a side has fewer rows, extend by randomly repeating avg_rows - current rows.
  • If a side has more rows, drop the last current - avg_rows rows.
• Perform a multi level sort on left and right rows. Use the same sort hierarchy as in the stitching phase.
• For i <- 1 to avg_rows:
  • Merge left and right rows by taking distinct columns from both sides.
  • For the shared stitch columns:
    • If either side is a "stitch owner", always use that side's stitch columns.
    • Else:
      • if i is odd, use the stitch columns from the left side.
      • If i is even, use the stitch columns from the right side.
  • Add the merged row to the resulting microtable.
• Return the microtable.

When building microdata for an ML target column, we run a simpler version of the cluster builder.

• Use sci-kit learn to find the best k ML features (columns with high ML relevance) for the target column.
• Create initial cluster which at first contains only the target column.
• Set initial cluster as the current cluster.
• For each feature in k-features (ordered by importance, highest to lowest):
  • If current cluster has available space:
    • Add feature to current cluster.
  • Else:
    • Start a new cluster and set it as the current cluster.
    • Add the target column as a stitch column to this cluster.
    • Add feature to current cluster.
• The first cluster is the initial cluster, the rest are derived clusters which are stitched by the target column.
• If patching is enabled:
  • For each non-feature column:
    • Add derived cluster with no stitch column and the single derived column [column].
• Return clusters.

In other words, we chunk the k-features by a max size. The target column is present in each one. We optionally patch the non-feature columns to get a complete table.

Noise is added to entity and row counts using an RNG seeded deterministically from the bucket's contents. This ensures identical output for the same data in different runs.

Entity threshold checks use a single noise layer that is seeded with the hash value of the AIDs included in the respective bucket.

Row counts have two noise layers: one seeded with the hash value of the AIDs present in the bucket and one seeded with the hash of the bucket's labels and columns (the column names in each tree make up the base seed of the tree and, for each bucket, the labels are made up from the middle-points of the bucket's ranges). Then, the regular Diffix count anonymizer runs.

A description of SynDiffix, its privacy properties, and its performance measured over a variety of datasets can be found at https://github.com/diffix/syndiffix/wiki/SynDiffix:-Overview.