Data Confidentiality

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If you’re publishing data, you’ll know how important it is to protect individual privacy and confidentiality


But in the Open Data Era, privacy means more than just removing Personally Identifiable Information

I know that my neighbour’s health records are included in an open dataset…

I know that she has three children and is in her mid 30s. By combining these facts, I can narrow down the results and easily locate her in the dataset.
Perturbation Example 1

Once I have located her…

It’s easy for me to discover confidential information about her medical history.
Perturbation Example 2



You Need Perturbation

To protect the people in your data, you need a robust confidentiality solution that automatically applies statistical disclosure control to everything you release.

We have worked in collaboration with government agencies around the world to develop a unique solution–perturbation–that addresses privacy concerns without compromising the flexibility or usefulness of your data.

Adjusts Cell Values

Perturbation automatically adjusts cell values slightly so it’s impossible to identify an individual.

Adds Smart Noise

It’s smart enough to know exactly where to add the noise so as not to affect the usefulness of the results.

Works Consistently

Specific values always get perturbed the same way, regardless of how you build the query/table.


Detailed Queries

Allows safe and secure access to detailed low level data queries.

The Right Balance

No need to choose between maximum protection or maximum data: perturbation is designed to keep your data as safe as possible and as useful as possible.

Fully Automated

Automatically applied on-the-fly to user queries. No subjective judgements. No need to manually adjust the results.



Next Steps…

Want To Know More?

Read our white paper to learn more about how perturbation can fit into your statistical disclosure control strategy.

Want to talk? Get in touch with us today to discuss how Perturbation can work for you.

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