Hi everyone,

I’ve released Obscura 0.1.1, an Elixir library for detecting and anonymizing personally identifiable information before it reaches logs, external APIs, analytics systems, or LLM providers.

The dependency-light core recognizes structured identifiers such as email addresses, phone numbers, credit cards, SSNs, IBANs, IP addresses, URLs, and domains. Optional local Nx/Bumblebee profiles add person, location, and organization recognition without requiring a hosted recognition service.

A basic redaction looks like this:

{:ok, result} =
  Obscura.redact(
    "Contact jane@example.com or call 202-555-0188",
    profile: :fast,
    entities: [:email, :phone]
  )

result.text
#=> "Contact [EMAIL] or call [PHONE]"

Obscura also supports nested Elixir data:

input = %{
  customer: %{email: "jane@example.com"},
  password: "secret"
}

{:ok, result} =
  Obscura.redact(input,
    entities: [:email],
    field_policies: %{password: :drop}
  )

result.data
#=> %{customer: %{email: "[EMAIL]"}}

The available operators are replacement, redaction, masking, hashing, pseudonymization, and application-defined callbacks. Pseudonymization can use an ETS-backed vault when an application needs to restore selected values after an LLM or external-service round trip.

The three stable profiles serve different use cases:

• :fast uses dependency-light deterministic and parser-backed recognition.
• :balanced adds one local model for general person, location, and organization recognition.
• :accurate uses a two-model cascade for the highest measured general accuracy.

Model-backed profiles require explicit preparation and reusable runtime resources. Normal analysis never downloads a model implicitly.

One important asset note: :balanced and :accurate currently use an OntoNotes-trained TNER checkpoint. Obscura does not bundle or license that checkpoint, and LDC confirmed that commercial use requires LDC for-profit membership. The dependency-light :fast profile is unaffected. The complete review is documented in the model-asset licensing guide.

Links:

• Hex: obscura | Hex
• Documentation: obscura v0.1.1 — Documentation
• Example workbench: GitHub - hfiguera/obscura_examples: Phoenix LiveView workbench demonstrating Obscura's public PII detection and anonymization APIs · GitHub
• Practical guide: Protecting PII in Elixir Before It Reaches Logs, APIs, and LLMs · Obscura
• Model licensing: Third-Party Dependency And Model Asset Review — obscura v0.1.1

This is the first public release, and I’d particularly appreciate feedback about the API, entity coverage, structured-data behavior, model preparation, deployment, performance, and use cases that the current profiles do not handle well.