Synthetic Data for GovTech
Finally there is a way to reconcile Big Data innovation with data protection!
The public sector collects vast amounts of personal information on employment, education, prices, taxes and many other aspects of a citizen's life. While safeguarding the privacy of every citizen is a key responsibility for government organizations, it is also in their interest to share this information - in an anonymized way - to foster startup innovation, facilitate research and to collaborate on the development of next-generation government services. But the anonymization techniques currently used strongly impede the utility of the shared data. What if there was another way?
Classic Anonymization Fails for Big Data
Truly anonymous data is exempt
from data protection regulations and thus free to use. But in the era of Big Data, classic anonymization does not protect against de-anonymization anymore. A famous example is, that 87% of US citizens were uniquely identified
by date-of-birth, gender and ZIP code alone. This risk is fully understood within the privacy community but is commonly underestimated by decision makers, who thereby put their organizations at financial, regulatory and reputational risk. As even the most sophisticated anonymization methods on the market fall short in the presence of Big Data, a fundamentally new approach is needed
Synthetic Data for Big Data Anonymization
AI-generated Synthetic Data is a game changer for Big Data anonymization, which allows to retain nearly all of the valuable information in a dataset, while at the same time protecting privacy 100%. This is made possible by leveraging state-of-undefinedthe-art generative deep neural networks that can automatically capture the structure and variation of an existing customer dataset. Then, after they were trained, they can be used to generate an unlimited number of highly realistic & representative synthetic customers that match the patterns and behaviors of your actual customers at an unprecedented level.
Synthetic Data is as-good-as-real but yet completely anonymous, allowing you to transform your privacy-sensitive big data assets into data that is free to use, free to share and free to monetize.
How The Synthetic Data Engine Works
Our Synthetic Data Engine is flexible
, easy to use
, scales to millions of protected customers
and is certified with the European ePrivacy Seal
. Since we know that your customers' data is one of your most sensitive assets and that keeping it safe & secure is of utmost importance to you, our software is deployed within your secure environment
- either on-premise or in your private cloud. Thereby, we make sure that your privacy-sensitive data never has to leave your organizational boundaries.
The synthetization process consists of two basic steps:
1. Training phase: The engine analyzes the existing data and automatically learns all the structures, correlations and time-dependencies within your customers' behavior.
2. Generation Phase: After the training is completed, you are able to generate an unlimited number of synthetic customers, which almost exactly match the patterns and behaviors of your original customers. An additional benefit is, that this step does not require the availability of the original data anymore, and thus can be executed in any environment at any later stage.