macOS App Offline Data Extraction
Budget: $30 – $250 USD
Job Title: macOS App Data Extraction Specialist (Offline QBank – Reverse Engineering Required)
Job Description:
We are seeking an experienced freelancer with expertise in macOS application data analysis and reverse engineering to help extract offline data from a medical QBank application installed on macOS.
The application allows users to download question banks for offline use, and the required data already exists locally within the app’s container directory on the Mac. However, the storage format appears to use a browser-style database system (e.g., IndexedDB/LevelDB) rather than a traditional SQLite database.
Your task will be to analyze the application’s local storage structure and extract the stored QBank content into a clean, structured dataset.
Scope of Work:
Analyze the macOS application’s local container storage and database files
Identify how the QBank data is stored within IndexedDB/LevelDB or similar storage systems
Perform reverse engineering of the application’s storage structure to understand how the data is encoded or organized
The encryption key used by the application is not currently available, so the process may require analyzing the app logic to determine how the data is decoded or accessed
Extract all available downloaded offline QBank data, including:
Questions
Answer choices
Correct answers
Explanations
Associated metadata (e.g., exam name, subject, topic, IDs, etc.)
Convert and deliver the extracted data in a clean, structured format (preferably JSON or CSV)
Requirements:
Strong experience with macOS application file systems and container directories
Experience analyzing IndexedDB, LevelDB, or other browser-based storage systems
Knowledge of reverse engineering techniques for desktop applications
Familiarity with data extraction, decoding, and structuring
Ability to document the extraction process clearly
Deliverables:
A complete structured dataset containing all extracted QBank data
Data provided in JSON or CSV format
Brief documentation explaining how the data was located and extracted
Additional Notes:
The goal of this project is strictly to extract already-downloaded offline data stored locally on the user’s device.
No server-side access or account bypassing is required.
If you have prior experience with app data extraction, reverse engineering, or analyzing local database structures, please include relevant examples of similar work in your proposal.
Job Description:
We are seeking an experienced freelancer with expertise in macOS application data analysis and reverse engineering to help extract offline data from a medical QBank application installed on macOS.
The application allows users to download question banks for offline use, and the required data already exists locally within the app’s container directory on the Mac. However, the storage format appears to use a browser-style database system (e.g., IndexedDB/LevelDB) rather than a traditional SQLite database.
Your task will be to analyze the application’s local storage structure and extract the stored QBank content into a clean, structured dataset.
Scope of Work:
Analyze the macOS application’s local container storage and database files
Identify how the QBank data is stored within IndexedDB/LevelDB or similar storage systems
Perform reverse engineering of the application’s storage structure to understand how the data is encoded or organized
The encryption key used by the application is not currently available, so the process may require analyzing the app logic to determine how the data is decoded or accessed
Extract all available downloaded offline QBank data, including:
Questions
Answer choices
Correct answers
Explanations
Associated metadata (e.g., exam name, subject, topic, IDs, etc.)
Convert and deliver the extracted data in a clean, structured format (preferably JSON or CSV)
Requirements:
Strong experience with macOS application file systems and container directories
Experience analyzing IndexedDB, LevelDB, or other browser-based storage systems
Knowledge of reverse engineering techniques for desktop applications
Familiarity with data extraction, decoding, and structuring
Ability to document the extraction process clearly
Deliverables:
A complete structured dataset containing all extracted QBank data
Data provided in JSON or CSV format
Brief documentation explaining how the data was located and extracted
Additional Notes:
The goal of this project is strictly to extract already-downloaded offline data stored locally on the user’s device.
No server-side access or account bypassing is required.
If you have prior experience with app data extraction, reverse engineering, or analyzing local database structures, please include relevant examples of similar work in your proposal.