Your organization's growing data assets promise to deliver new insights, foster innovation and enable smarter services. But how can you seize the big data opportunities, while not putting the privacy of your customers at any risk? You neither want to face the costs of a data breach, nor want to hold back progress.
But even the most sophisticated anonymization methods on the market fall short in the presence of big data, as they can only retain a small fraction of information, and thus of the value of your organization's data.
This calls for a fundamentally new approach!
Mostly AI has developed game-changing new technology that enables you to simulate an unlimited number of realistic & representative synthetic customers, matching the patterns and behaviors of your actual customers. It leverages generative deep neural networks to learn and thus retain detail, structure as well as variation of your privacy-sensitive data at an unprecedented level, while rendering the re-identification of any individual impossible.
By using synthetic data you are free to model, to explore, to experiment, to share, to archive, to sell and to modify the data as you see fit, and thus is opening up a whole range of otherwise inaccessible use cases and opportunities for your organization, that can now be safely pursued.
Provide realistic and representative data, instead of privacy sensitive production data, to your internal developers as well as external partner network. Instantly sharing a rich and accurate representation of the actual data will boost the development process, help design smart UX, improve testing, and make complex end-to-end integrations less error prone.
A large share of data assets remains locked up and inaccessible to a broader group of highly talented people due to privacy restrictions and data retention regulations. Being able to share realistic, representative data at any point in time opens up new possibilities for much closer collaborations with research institutions, startups, AI engineers, and innovation partners alike.
Running analytical queries as well as building machine learning models require access to large amount of record-level data. Differentially private synthetic data allows the unrestricted provision of such data, without running the risk of exposing individual’s privacy. Computation can take place on less restrictive environments by a larger group of people.
Openly share and/or monetize on your big data assets without putting privacy at risk, either directly or via emerging data marketplaces. Data providers can offer data at its fullest detail, with a high share of original information retained, and thus can provide much higher value to their data consumers compared to existing anonymization techniques.
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