Brief description

Onboarding a new data set : - analyze it and set anonymization processes - connect the Data owner IT with Truata platform - ingest the data and execute the planned anonymization steps - pass acceptance tests Periodically: - ingest the data and execute the planned anonymization steps Building a model on the anonymized data set - use the analytics tool to train and validate the model - export insights - exports are Privacy-tested before they are allowed We are not aware of any geographical restriction to the usage of our Service in the EU.

Main Features

By a mix of technoogy and legal elements: Truata is set up as an independent company in the form of an anglo saxon Trust to act as an independent controller of the anonymization process. Truata has presented its unique construct to many DPAs in Europe and the Data protection Supervisor of the European Commission, and has received positive feedback about its approach and expertise. Our platform would: - Ingest data from multiple parties. - Perform standardised privacy risk assessment on all ingested data. - Apply anonymisation techniques to reduce re-identification

Areas of Application

Health

Market Trends and Opportunities

Truata Anonymization Service anonymizes a data set in compliance with GDPR requirements, to enable analytics that require working on individual data without allowing for re-identifications of the said individuals. We would produce analytics specifically optimised for coronavirus research, including: - Disease progress prediction and analysis - Effectiveness and impact of containment measures - Recommendation of measures for relaxing lockdown restrictions Truata solution uniquely balances the conflicting needs for high-precision data analysis and the need to protect the privacy of individuals, which can help build trust for the program.

Customer Benefits

Truata would be an enabler for analysts that need to process individual data (provided by a third -party source).

Technological novelty

Truata could anonymize up to five (5) data sets in parallel