wipescrape

Wipescrape is a neologism used in some IT and data privacy discussions to describe a combined workflow that encompasses two distinct operations: data wiping and data scraping. In this usage, wiping refers to securely erasing or anonymizing sensitive data from a dataset, device, or storage medium, while scraping refers to programmatically collecting data from publicly accessible sources such as websites or APIs. The term is not part of a formal standard and its meaning can vary by context, but it typically denotes an approach that balances data sanitation with data collection for research or analytics.

Applications include: preparing datasets for machine learning by removing personal or sensitive information before or after extraction; testing and validation workflows that require both sanitization and data harvesting; privacy-preserving data pipelines where sensitive content is wiped and non-sensitive data is then scraped for analysis.

Methods: Wiping often involves secure deletion methods, cryptographic erasure, or data minimization practices; scraping involves parsing HTML, API consumption, and structured data extraction. Privacy and legality: Wipescrape activities must comply with laws such as GDPR and CCPA, respect terms of service, and consider user consent and copyright.

Criticism centers on potential ambiguity, risk of over-scraping, and the challenge of proving compliance in mixed workflows. The term remains informal and is primarily used in discussions about data lifecycle management and privacy-by-design approaches.