> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datafog.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 0.3.0

> Structured PERSON discovery and faster finding selection and text-position handling.

DataFog Core 0.3.0 adds structured PERSON discovery and improves performance
when processing many findings. Rust, Python, Node.js, and browser WASM share
the detection and stateless protection behavior.

## Structured PERSON support

* Discover explicit name fields such as `first_name`, `last_name`, and
  `fullName` without a model or dictionary download.
* Supply concrete JSON Pointer mappings for fields that automatic discovery
  leaves unresolved, such as `/customer/name`.
* Scan JSON string values with the original seven detectors and return each
  finding with its field path. Protect the findings with the existing policies.
* Use structured pseudonymization, tokenization, and restoration through
  Rust, Python, and Node provider integrations. Browser WASM retains its
  existing restriction on provider-backed operations.

PERSON detection uses field context. It does not recognize arbitrary names in
prose, and passing serialized JSON to `scan(text)` does not enable discovery.
See [Discover and protect person fields](/guides/person-discovery).

## Performance

* Index duplicate findings and resolve ordinary overlaps with ordered interval
  selection. Built-in findings use an O(m log m) selection path, where m is the
  finding count.
* Reuse lazy text indexes during validation and byte/code-point/UTF-16 range
  conversion, maintain running output positions, and avoid copying a whole
  field for every Node transformation record.
* Expose Rust's reusable `TextIndex` for converting multiple ranges from the
  same string. See the [Rust reference](/reference/rust).

On one local macOS ARM64 benchmark, selection of 4,096 disjoint findings fell
from 60.3 ms to 0.56 ms. Separately, the bookkeeping changes reduced a complete
Node structured scan-and-protect request with 1,024 findings in one Unicode
field from 242.3 ms to 3.2 ms. These measure different stages and baselines;
they are not a universal speedup guarantee. Short-field workloads changed
little, and one sparse scan-only case was about 10% slower.

The [selection benchmark notes](https://github.com/DataFog/datafog-core/blob/main/docs/finding-selection-performance.md)
and [bookkeeping benchmark notes](https://github.com/DataFog/datafog-core/blob/main/docs/bookkeeping-performance.md)
include inputs, methodology, and limitations.

## Compatibility and publishing

Existing validation, offset semantics, transformation policies, and finding
preferences are preserved. Caller-supplied overlapping findings that mix
scored and unscored confidence can retain the original quadratic selection
algorithm to preserve its behavior. See [Findings and ranges](/concepts/findings-and-ranges).

The Node and WASM release workflows publish to npm from GitHub Actions using
trusted publishing. Node builds cover macOS ARM64/x64, Linux GNU ARM64/x64,
and Windows x64. Node.js 24.x remains required.

This release contains [PR #12](https://github.com/DataFog/datafog-core/pull/12),
[PR #13](https://github.com/DataFog/datafog-core/pull/13), and
[PR #14](https://github.com/DataFog/datafog-core/pull/14).