# 1. Upload archive with open("evidence_bundle.rar", "rb") as f: resp = requests.post(f"BASE_URL/extract", files="file": f, headers=headers) job_id = resp.json()["job_id"]
This article examines the evolution of DeproRED, its core capabilities, system requirements, installation process, typical usage scenarios, and a balanced assessment of its strengths and limitations. | Year | Milestone | |------|-----------| | 2015 | DeproRED 1.0 launched as a Windows‑only command‑line tool for batch RAR extraction. | | 2017 | Added basic regex‑based redaction for text files. | | 2019 | Introduced multi‑platform support (Linux & macOS) via a bundled Java runtime. | | 2021 | Version 3.x integrated a machine‑learning model for entity detection (names, SSNs, credit‑card numbers). | | 2023 | DeproRED 4.0 overhauled the UI, added a REST API, and introduced parallel extraction pipelines. | | 2024 | 4.1 (current) refines the ML model, expands file‑type coverage, and adds granular audit logging. | DeproRED 4.1.rar
BASE_URL = "http://localhost:8080/v1" headers = "Authorization": "Bearer <API_TOKEN>" | | 2017 | Added basic regex‑based redaction
import requests, json