Cities are some of the most important actors in the future data ecosystems. Cities are hubs that collect data from various sources, for instance from health care, traffic, public transport, energy production and consumption, schools and education, housing etc. Much of these data are sensitive by nature – they contain information of private people or of other data subjects that may not be revealed.

It is obvious that these data resources are interesting and valuable not only in the primary purpose that they have been collected for but also in various secondary use cases.

Some of the use cases are internal. Typically these internal uses are linked to data-intensive management, better insights and development of services and processes. Some of the use-cases are targeted to serve non-profit organizations like for instance researchers, journalists, third sector actors and statistical organizations. Some are serving commercial purposes – building new innovations that are utilizing and often combining different data sources.

However, none of these secondary use cases is possible before data have been thoroughly anonymized or synthesized.

VEIL.AI anonymization technology can be applied to city data context easily and by many ways to reach GDPR compliant and safe use of data:

1) To analyze data sets from a privacy point of view – to heat map if and where there are variables and variable combinations that carry privacy risks.
2) To define interactively an optimal data anonymization strategy – a data transformation process that minimizes privacy risks and information loss simultaneously.
3) To produce synthetic data that prevails the interesting features of the underlying data but hides all sensitive features
4) To integrate data anonymization or synthetization competence to different architectures and pipelines.

VEIL.AI anonymization engine supports several data types:

1)     Registry data
2)     Structural static
3)     Structural time-series data
4)     Real-time data
5)     Data derived from unstructured data (e.g. picture data, genomic data etc)

READ MORE about our project where we support secondary use of mobility data collected for city planning purposes. We anonymize the time-location data.

Ask Tuomo more about anonymization

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