anonymized data

USAGE SUMMARY

The phrase "anonymized data" is correct and usable in written English.
It can be used when referring to data that has been processed to remove personally identifiable information, making it impossible to identify individuals from the data. Example: "The research study utilized anonymized data to ensure the privacy of participants while still allowing for valuable insights."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The researchers used aggregated, anonymized data provided by Orange, British Telecom , Telecom Italia and the Saudi Telecom Company.

MIT Technology Review

The anonymized data Green did see looked plausible and convincing.

The New Yorker

The anonymized data did not include identifying personal information.

The Washington Post

Our data science team analyzed the anonymized data to uncover elusive measures of work satisfaction.

Harvard Business Review

These are also available at the data hub for the study's anonymized data and analysis.

Harvard University

The team has made all of their anonymized data, analysis, and code publicly available for review.

Harvard University

They have made all of their anonymized data, analysis, and code publicly available for review.

Harvard University

Participating organizations use Distributed App (an interface to blockchain contract) to access the anonymized data through smart contracts.

Harvard Business Review

Aggregated or anonymized data might indeed reveal sensitive information on groups and could lead to stigmatization or discrimination.

Nature

Habegger, L. et al. RSEQtools: a modular framework to analyze RNA-Seq data using compact, anonymized data summaries.

Nature

The anonymized data are dispersed on hadoop distributed file system.

Data Science and Engineering

Expert writing Tips

Best practice

When discussing research or data sharing, explicitly state the methods used to create the "anonymized data" to ensure transparency and build trust.

Common error

Don't overestimate the effectiveness of anonymization techniques. Even "anonymized data" can sometimes be re-identified through cross-referencing with other datasets. Always assess and mitigate this risk.

Linguistic Context

The phrase "anonymized data" functions as a noun phrase, where "anonymized" acts as an adjective modifying the noun "data". It describes a specific type of data that has undergone a process to remove identifying information. As Ludwig AI confirms, this expression is correct and usable.

Expression frequency: Very common

Frequent in

Science

35%

News & Media

33%

Formal & Business

17%

Less common in

Academia

10%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "anonymized data" refers to data from which personally identifiable information has been removed. This phrase is grammatically correct and very commonly used across various fields, including science, news, and business. As Ludwig AI suggests, it is considered correct and used to denote data that protects individual privacy. Related phrases include "de-identified data" and "masked data". When using "anonymized data", ensure transparency by stating the methods used for anonymization and avoid overestimating its effectiveness, as re-identification is still possible. The frequency of the expression is high, making it a familiar and recognized term in discussions about data privacy and security.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

de-identified data

Replaces "anonymized" with a synonym emphasizing the removal of identifying information.

non-identifiable data

Directly states the data cannot be used to identify individuals.

redacted data

Indicates specific information has been removed or obscured for privacy.

masked data

Suggests the data has been altered to hide sensitive details.

privacy-protected data

Highlights the safeguarding measures taken to ensure data privacy.

obfuscated data

Implies the data has been made unclear or unintelligible to protect privacy.

unattributed data

Focuses on the lack of connection between the data and any specific individual.

depersonalized data

Emphasizes that the data no longer contains personal identifiers.

aggregate data

Focuses on data combined from multiple sources, obscuring individual contributions.

sanitized data

Highlights the removal of potentially harmful or sensitive elements from the data.

FAQs

How is "anonymized data" used in research?

"Anonymized data" is used to perform studies and analysis without compromising the privacy of individuals. It allows researchers to identify trends and patterns without accessing personal information.

What's the difference between "anonymized data" and personal data?

"Anonymized data" has had all personally identifiable information removed, making it impossible to trace back to an individual. Personal data, on the other hand, includes information that can directly or indirectly identify a person.

Can "anonymized data" ever be re-identified?

While the goal of anonymization is to prevent re-identification, it's not always foolproof. Advanced techniques and access to other datasets could potentially lead to re-identification in some cases. Therefore, robust anonymization methods are crucial.

What are some alternatives to using "anonymized data"?

Depending on the context, you can use alternatives such as "de-identified data", "redacted data", or "masked data".