Introduction

AMNESIA is a flexible data anonymization tool that allows to remove identifying information from data. It does not only remove direct identifiers like names, SSNs, etc., but also transforms secondary identifiers like birth date and zip code. This way individuals cannot be identified in the data. Amnesia supports k-anonymity and km-anonymity, two privacy guaranties that make each record indistinguishable from other k-1 records. Km-anonymity is a weaker form of k-anonymity that is better suited for high-dimensional data. Amnesia is available both as an online service and as a local application.

What is Amnesia?

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Amnesia is the data anonymization tool of OpenAIRE that allows you to remove identifying information from data. It is a flexible data anonymization tool that transforms relational and transactional databases to datasets where formal privacy guaranties hold. Amnesia transforms original data to provide k-anonymity and km-anonymity: the original data are transformed by generalizing (i.e., replacing one value with a more abstract one) or suppressing values to achieve the statistical properties required by the anonymization guaranties.
With this high accuracy data anonymization tool, you can perform research and share your results while satisfying GDPR guidelines by making use of Amnesias data anonymization algorithms.

Why Anonymization?

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Data anonymization may enable the transfer of information across a boundary. This can be another country or research project but also another department or person. Data anonymization seeks to protect private or sensitive data by deleting or encrypting personally identifiable information from a database. It facilitates the publication of micro data (vs. aggregated macrodata), e.g., data used in scientific research. Micro data often reveal important private information, such as personal information or the medical condition of a person. Even when some of the data fields, such as names, are replaces with abstract concepts, through combination of different datasets it may still be possible to trace it back and identify a person.

Therefore anonymization should be done with anonymization guaranties over the entire dataset. Anonymization might also help individuals who are afraid to provide their data or companies reluctant to share data with experts because GDPR rules makes a strict protection scheme obligatory. Anonymization methods allow sharing such data, without compromising the privacy of the users. You can find more information in our sensitive data guide.

 

Features

Unlock sensitive data analysis
You can use Amnesia to transform personal data to anonymous data that can be used for statistical analysis. Data anonymized with Amnesia are statistically guaranteed that they cannot be linked to the original data.

Becoming GDPR compliant
Creating anonymous datasets from personal data is done in such a way that they are treated as statistics by GDPR. Anonymous data can be used without the need for consent or other GDPR restrictions, greatly reducing the effort needed to extract value from them.

High Usability & Flexibility
Anonymization is tailored to user needs through a graphical interface. You can guide the algorithm and decide trade-offs with simple visual choices. Developers can incorporate Amnesia anonymization engine to their project through a ReST API.

How it works

Get anonymous data in 3 steps:

  1. Insert your data
    Amnesia accepts complex object relational data in delimited text files.
  2. Select and Preview the data to anonymize
    Visual representations of anonymization parameters and results allow non-expert users to tailor the anonymization process to their needs.
  3. Download your data anonymized
    The process is completed without any sensitive data leaving your premises!

If you like to test it out, there is an online version mostly used for demonstration and testing purposes. Sensitive data can be anonymized locally by downloading the application.

Try it here: https://amnesia.openaire.eu/

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open data, e-infrastructure, sensitive data, amnesia

OpenAIRE
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funding from the European 
Union's Horizon 2020 Research and
Innovation programme under Grant
Agreement No. 777541.

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