January 28 is Data Privacy Day 2021 and the opportunity to raise awareness around data privacy and the protection of personal information. We prepared a list of events and resources on these topics.
This post presents different synthetic data types: text, media (video, image, sound), and tabular synthetic data. We define them, go over the reasons behind the need for synthetic data, and present five real-life examples of synthetic data applications.
How do you solve the joint issues amongst different relational tables? How do you kickstart a synthetic data project? Which industries have the strongest need for synthetic data? Read on to discover the answers to our latest webinar questions in this post!
This post revisits the story of Newsenselab's project with synthetic data technology. Read how the team successfully anonymized more than 170 000 migraine symptom data points while maintaining a high utility for research.
This post sheds light on a data type gaining in popularity: time-series data. It offers a definition and presentation of this analytics resource. We explore the challenges of leveraging time-series data and how synthetic time-series data generation could help.
This post explores the challenges of leveraging data in the financial industry, as well as how to regain the ability to work with data safely and efficiently. Read how synthetic financial data can support your organization data operations while complying with existing data privacy constraints.
Are you evaluating privacy-preserving synthetic data and want to know what Return on Investment (ROI) you can expect? Check out this article in which we cover some elements to take into account when evaluating the value of adding synthetic data generation capabilities to your team’s toolbox.
We discussed the challenges and costs associated with data inertia during our recent webinar on synthetic data for finance. We took some of the interesting questions that we received from the audience and presented the answers in this article.
Privacy-enhancing techniques can help companies answer to ever-increasing privacy requirements of technical and societal nature. This article presents privacy protection methods such as pseudonymization, generalization, and generation of synthetic data. It also discusses the scope and importance of data privacy.