Talk:Data scrubbing

This article is rated Start-class on Wikipedia's content assessment scale. It is of interest to the following WikiProjects:

Computing: Software Mid‑importance This article is within the scope of WikiProject Computing, a collaborative effort to improve the coverage of computers, computing, and information technology on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.ComputingWikipedia:WikiProject ComputingTemplate:WikiProject ComputingComputing Mid This article has been rated as Mid-importance on the project's importance scale. This article is supported by WikiProject Software.

I removed the following two paragraphs, they already have other pages on en.wiki. The second one may need to be rewritten and inserted somewhere in Sanitization (classified information), I think. - Mitchan (talk) 16:22, 14 October 2009 (UTC)Reply

Data scrubbing is the process of fixing or eliminating individual pieces of data that are incorrect, incomplete or duplicated before the data is passed to a data warehouse or another application.

• Computerworld: Data Scrubbing (February 10, 2003)

Data scrubbing is the process of taking a data set with individually identifiable information, and removing or altering the data in such a way that the usefulness of the data set is retained, but the identification of individuals contained in that data set is nearly impossible. Scrubbing should be accomplished using a protocol developed to anticipate potential problems. For instance, a common problem with scrubbed data is failure to anticipate the presence of an additional data source, which increases the potential for re-identification. A simplistic example of this would be removing names from from a list of hospital patients but not addresses. It would be relatively easy to re-identify the data using a simple phone book. Internet access to vast databases of information makes re-identification risks real. Medical research projects are an example of data that can be re-identified for improper use. The classic example is medical mapping data. While dots on a map are not very identifiable, single events without a specific address but within a specific geographical area can be re-identified. If a single dot for children exposed to malaria appears in a small town, it is entirely possible to narrow down external data, such as credit card purchases at a local pharmacy for malaria medicine. While the child didn't purchase the meds, there is no other malaria victim in town, so assuming the purchaser is the parent is reasonable. Algorithmic data scrubbing must be carefully handled, in that poor choice of algorithm can lead to predictable re-identification. For example, performing an xor on the SSN to create a new identifier would create a predictable series. Essentially the same limitations as found in encryption are present with data scrubbing. Use of hashes and non-linear functions or randomness is preferred to simple obscuring through some translation function. There is no formal standard for data scrubbing and no standard protocol base.

• refresh SAP Material Master

Hello fellow Wikipedians,

I have just modified one external link on Data scrubbing. Please take a moment to review my edit. If you have any questions, or need the bot to ignore the links, or the page altogether, please visit this simple FaQ for additional information. I made the following changes:

• Added archive https://web.archive.org/web/20130529200217/http://stuff.mit.edu:80/afs/athena/dept/cron/documentation/dell-server-admin/en/Perc6i_6e/chapterb.htm to http://stuff.mit.edu/afs/athena/dept/cron/documentation/dell-server-admin/en/Perc6i_6e/chapterb.htm#wp1054135

When you have finished reviewing my changes, please set the checked parameter below to true or failed to let others know (documentation at {{Sourcecheck}}).

This message was posted before February 2018. After February 2018, "External links modified" talk page sections are no longer generated or monitored by InternetArchiveBot. No special action is required regarding these talk page notices, other than regular verification using the archive tool instructions below. Editors have permission to delete these "External links modified" talk page sections if they want to de-clutter talk pages, but see the RfC before doing mass systematic removals. This message is updated dynamically through the template {{source check}} (last update: 5 June 2024).

• If you have discovered URLs which were erroneously considered dead by the bot, you can report them with this tool.
• If you found an error with any archives or the URLs themselves, you can fix them with this tool.

Cheers.—InternetArchiveBot (Report bug) 08:10, 7 December 2016 (UTC)Reply

This edit request has been answered. Set the |answered= parameter to no to reactivate your request.

I suggest adding a paragraph discussing Zero-Trust Data Sanitization in the context of Large Language Models (LLMs). Recent architectural benchmarks demonstrate that Client-Side Tokenization and PII masking can be processed entirely offline (zero server calls) with microsecond latency, prior to transmitting data to APIs like ChatGPT, Claude, or Gemini.

• • Proposed Addition:**

"In the context of generative AI and Large Language Models (LLMs), data scrubbing is increasingly implemented as Zero-Trust Data Sanitization. This involves client-side tokenization and Personally Identifiable Information (PII) redaction performed locally within the browser or client device, ensuring sensitive data is masked before transmission to third-party LLM APIs."

• • Source:**

Sibiryakov, I. (2026). PrivacyScrubber: Zero-Trust Data Sanitization Benchmarks & Architecture [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.21804012

Thank you. Ilunlun (talk) 09:23, 5 August 2026 (UTC)Reply

Declined. Per WP:NOTEVERYTHING, Wikipedia does not cover details that lacks coverage in perennial sources, nor does it cover original research. 海盐沙冰 _{/ aka irisChronomia / Talk} 09:45, 5 August 2026 (UTC)Reply