GoCalma vs Philter

GoCalma is open-source browser software for detecting and redacting sensitive information in PDF files without sending document contents to a server. It identifies more than 30 personal-information types and validates formats across more than 10 European ID formats. Users can choose redaction, masking, hashing, highlighting, or synthetic replacement by information type, then review candidates with the strategy, pattern, and confidence behind each flag. OCR for scanned or image-heavy PDFs runs locally. Listed profiles include Swiss DPA, EU GDPR, Healthcare, Financial, and Full Scan. An encrypted .gocalma recovery key can restore the original document later with the matching key. The repository says key payloads use AES-GCM encryption and redaction removes PDF author, title, and timestamp metadata. Its audit summary records a design-level concern involving raw key storage when no password is set. The Vercel demo uses regex-only detection because model files exceed its 250 MB limit. Local setup requires Node.js 18 or later, npm, and a modern desktop browser. The software is distributed under the MIT License.

Philter is open-source software for finding and redacting personally identifiable information and protected health information in text. For detected entities, users can choose to mask, encrypt, replace with synthetic values, remove, or pass them through. Policies, custom dictionaries, and identifier patterns such as medical record numbers and transaction IDs let teams configure what to detect and how to handle it. General Purpose, Healthcare, and COVID-19 NLP lenses address different types of text. Philter provides an HTTP REST API, a Java SDK, and an OpenAPI specification for generating clients in other languages. It can run in a private environment or through AWS, Google Cloud, and Microsoft Azure marketplaces. The maker says text stays in the customer’s environment, including firewalled or air-gapped deployments. Redaction requests are recorded in a tamper-evident, hash-chained audit log that can be queried and exported to CSV. Detection is probabilistic, so results should be checked against an organization’s own data before production use. The open-source toolkit has community support; commercial licenses include direct support.