Syntho is live with their synthetic data proposition

Why Syntho?

We are witnessing two major trends happening today. The first trend describes the exponential growth of the use of data by institutions, governments and customers. The second trend describes the growing concern of individuals about their ability to control the information they reveal about themselves, and to whom. On the one hand, we are eager to use and share data to unlock enormous value. On the other hand, we want to protect the privacy of individuals, which is typically accomplished by placing restrictions on the use of personal data, mainly through legislation, such as the GDPR. This phenomenon, we denote as the ‘privacy dilemma’. It is the impasse where the use of data and the privacy protection of individuals unremittingly collide.

Illustration 1

It is our purpose at Syntho to solve your privacy dilemma with and for you.​

Syntho – who we are?

Syntho – AI-Generated Synthetic Data

As three friends and founders of Syntho, we believe that artificial intelligence (AI) and privacy should be allies, not enemies. AI has the potential to help solve the global privacy dilemma and is the secret sauce of our privacy-enhancing technology (PET) that enables you to use and share data with privacy guarantees.  Marijn Vonk (left) has a background in computing science, data science and finance and has been working as a consultant in fields of strategy, cyber security and data analytics. Simon Brouwer (center) has an education in artificial intelligence and has experience in working with large amounts of data as a data scientist within a variety of companies. Wim Kees Janssen (right) has a background in economics, finance and investments and is proficient as a product manager and strategy consultant.

Our Syntho engine for generating Synthetic Data

Syntho has develped a deep learning-based privacy-enhancing technology (PET) that can be used with any type of data. After training, our Syntho Engine is able to generate new, synthetic data that is completely anonymous and preserves all the value of the original data. Synthetic data by Syntho has two key attributes:

• <em>It is impossible to reverse-engineer individuals in privacy-preserving synthetic data</em><br>Our Syntho Engine has a built-in mechanism involving ‘differential privacy’ to infer that the dataset contains no records from the original dataset and that no individuals can ever be identified.

• <em>Synthetic data retains the statistical properties and structure of the original data</em><strong><br></strong>The Syntho Engine captures all the relevant properties and structures of the original data. Hence, one experiences similar data utility with the synthetic data as with the original data.

Illustration 2

Synthetic Data Generation