Enabling use of sensitive data

We are committed to safe-keeping individual privacy in big data analytics and machine learning applications.

Maximum privacy protection and data utility. Our novel methods maximize anonymity while retaining the usefulness of the data.

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With VEIL.AI your sensitive data can serve: 

  • New data-intensive innovations 
  • Research & development
  • Management by knowledge
  • Machine Learning applications
  • Open data 

VEIL.AI Anonymization engine

It is a novel, powerful approach to de-identify personal or otherwise sensitive data, facilitate sharing and analyzing data in low or zero-trust environments and ensure that neither anonymity nor data quality is compromised. 

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VEIL.AI anonymization engine can be used at three levels:

  • One-off anonymizations (piloting, research projects etc)
  • Continuous anonymization service (e.g. biobanks data access point)
  • Part of system architecture (e.g. anonymization competence and UI implemented into hospital data lake

Our technology:

DE-IDENTIFIES DATA

BRINGS TOGETHER BIG DATA

PRODUCES SYNTHETIC DATA

We can handle several interesting data types:

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Structured data

  • Biobank data
  • Registry data
  • Clinical data
  • Survey data
  • Time series data

Unstructured data

  • Geo-location
  • Genome data

Our four categories of Sensitive data

SENSITIVE DATA

PSEUDYNOMIZED DATA

ANONYMIZED DATA

SYNTHETHIC DATA

HIGH RISK

MEDIUM RISK

LOW RISK

NO RISK

John – 45 y/o

Original data with personal identifiable information

444555 – Male

Data with all personal identifiers encoded

444555

Data with identifiable information transformed

Max – Male

Statistical data based on real data and data models

Our four categories of Sensitive data

SENSITIVE DATA

PSEUDYNOMIZED DATA

HIGH RISK

MEDIUM RISK

John – 45 y/o

Original data with personal identifiable information

444555 – Male

Data with all personal identifiers encoded 

ANONYMIZED DATA

SYNTHETHIC DATA

LOW RISK

NO RISK

444555

Data with identifiable information transformed

Max – Male

Statistical data based on real data and data models

INFORMATION PACKAGE

Management by knowledge
Machine Learning applications 

Use cases

HEALTH DATA

Healthcare industry produces massive amounts of sensitive data that would be valuable in building knowledge-based management, predictive analytics, and real-time monitoring applications.  This data cannot be fully utilized before it has been thoroughly anonymized because it reveals a unique portrait of an individual and is often spread out across hospitals, clinics, and research institutions.

Typical clients:

  • Researchers
  • HealthTech companies
  • Pharma companies
  • Hospitals
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CITY DATA

Modern teaching and digital classroom solutions generate a lot of data, which can be used to analyze different factors of the learning process, to create personalized learning for each individual and to offer tools for education planning and management.

Typical clients:

  • Transport authorities
  • City planners
  • Schools and cities
  • EdTech companies
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