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Data sharing can transform service delivery and boost efficiency, but data privacy is a major barrier. Traditional de-identification methods often fall short, leaving data vulnerable to re-identification attacks.

OptimShare is designed to overcome this challenge. It enables privacy-preserving data sharing between organisations without compromising data utility.

OptimShare framework

Unlike blunt data censorship methods, OptimShare replaces sensitive details with contextually relevant, non-identifiable alternatives. Data remains useful, confidential, and ready to inform better planning and decision-making. Learn more about OptimShare.

Ready to rethink how data is shared?

Submit your expression of interest today:

Data sharing can transform service delivery and boost efficiency, but data privacy is a major barrier. Traditional de-identification methods often fall short, leaving data vulnerable to re-identification attacks.

OptimShare is designed to overcome this challenge. It enables privacy-preserving data sharing between organisations without compromising data utility.

Flowchart showing a data anonymisation process using a "PIF Tool" within an AI-enabled framework. The process starts with data owners providing an input dataset to an administrator, who feeds it into the framework. Inside the framework, the dataset undergoes risk assessment, parameter definition, data perturbation, and data instance generation. The perturbed data is then shared with analysts or third parties for analysis.
The OptimShare framework

Unlike blunt data censorship methods, OptimShare replaces sensitive details with contextually relevant, non-identifiable alternatives. Data remains useful, confidential, and ready to inform better planning and decision-making. Learn more about OptimShare.

Ready to rethink how data is shared?

Submit your expression of interest today:

Learn more about OptimShare

Enabling privacy-preserving data sharing between organisations