Blog

This article explores synthetic data generation. What's the logic behind synthetic tabular data generation? And what role can deep learning play? We look at two models, VAEs and GANs, and explain which approach we decided to follow at Statice.

This post is part of a series of discussions with team Statice. Beatrice and Ben from our commercial team looked back at their experiences working with enterprises on their data access and privacy challenges. They shared their learnings and opinions on the evolution of data constraints.

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.

We are continuing our series of interviews with data and privacy experts. We asked 5 questions to Javier Franco-Contreras. He's CEO and co-founder of WaToo and a specialist in data protection and watermarking technology.

Are you wondering if Statice has the right synthetic data solution for your needs? In this post, we discuss some of the advantages of working with our data anonymization software. From integration to evaluation, our data anonymization solution has everything to fit your team’s requirements.

Learn about Statice’s participation in Startup Creasphere digital health program. We discuss the data challenges in the healthcare industry and our collaboration with Roche to test the use of synthetic medical data for clinical research.

In this post, we list 10 use-cases for privacy-preserving synthetic data. Learn how enterprises are leveraging synthetic data to generate value.

Read how a Swiss insurance company implemented privacy-preserving machine learning to future-proof its data operations. La Mobilière validated the use of synthetic data in the context of data privacy protection, adding a new tool in their digital transformation toolbox.

This blog presents the concept of privacy-preserving synthetic data. Learn about the definition of synthetic data, its origin in the context of data privacy, and its benefits for organizations.

What constitutes data anonymization is often debated within the scientific community. Regulators frequently revise the definitions of anonymized data within legal frameworks such as GDPR. So it is safe to wonder, what exactly is anonymized data? And is anonymized data truly anonymous?

Discover the landscape of Privacy Enhancing Technologies (PETs) and some of their applications. Building upon the concept of Privacy by Design, these technologies hold many opportunities for organizations, such as compliance for instance.

This is the first in a series of interviews we have planned with individuals who leverage data in their everyday work. We’re stoked to kick off the series with none other than Sunaina Swaminathan, Head of Data Acquisition at M Science! 

Today’s blog post comes from Statice’s newest hire, Peter. Peter joined Statice with a diverse background in several B2C businesses, and wanted to share some of his insights on getting up to speed in the very specific niches of digital transformation, privacy-preserving data science and machine learning.

We recently hosted our first webinar on data privacy and digital transformation, and wanted to share the slides and some great audience questions on Statice vs federated learning, brute forcing anonymization methods, and maintaining outliers in synthetic data.

We'll be hosting our first webinar on the 14th of April, discussing privacy and it's relevance in the age of data and digital transformation. Read on for more details on the topic and for sign up!

If you need to unlock sensitive data for CV19 research, Statice is offering its software for data anonymization to support research and development of solutions to detect, understand, or help treat Covid-19.

Statice offers a data anonymization software that enables companies to easily use sensitive data while meeting the legal requirements of modern data privacy regulations. The Statice software generates synthetic data that allows companies to perform meaningful data analysis without ever touching the original data.

Statice and Panter are entering a partnership to enable privacy-preserving data innovation within the Swiss corporate ecosystem.

Statice won the audience prize in the 2019 President’s Startup Challenge for innovating and transforming the life science R&D. The Pistoia Alliance is a global non-profit community of life science stakeholders that works to lower barriers to R&D innovation.

Many companies are still applying pseudonymization for data protection, thinking that they are protecting their customers’ data and perhaps even that they are being legally safe. Modern anonymization, contrary to pseudonymization, ensures that the data can never be retrieved by any means.

Statice is supporting the privacy efforts of Swiss corporates through the Kickstart program 2019. Follow us on Twitter @StaticePrivacy for more updates.

More fines are likely to be issued as data protection offices are beginning to flex the powers bestowed upon them under the GDPR. These events should serve as a reminder to companies dealing with sensitive data that data security is non-trivial, and that management of data security risks should be a clear organizational priority.

At Statice, we build software to safely anonymize sensitive personal information, which means that companies can protect individual privacy without giving up the potential for innovation and value creation around data. We believe that the ethical and moral responsibility to define concepts such as “good” and “bad” should always lie on humans and not the machines.

Companies are now forced to introduce new methods of data processing. Any solutions must have privacy by design — and it’s not always easy to know where to start. Statice has prepared answers to some common misconceptions and questions your company might have about data privacy, data anonymization, synthetic data, and more.

By creating synthetic data, Statice is enabling M-sense to freely share their data for research purposes — while fully preserving the privacy of the individuals involved. Statice and M-sense are together proving that hypersensitive data can be anonymized without any significant loss of quality or statistical significance.

Statice started in the health sector. They helped several health apps to analyze their data and pass it on to research. “The companies have a better product and doctors can now evaluate thousands of patient data they wouldn’t otherwise have.” A win-win situation.

We strongly believe that data driven collaboration both within and between organizations is a key driver of development, innovation and revenue. We’re already moving, with several key hires in the pipeline, and some very exciting projects coming shortly with leading healthcare, finance and automotive companies both from within Germany and internationally.

We empower companies to open up their new synthetic data in a GDPR-compliant manner for product development, training new machine learning algorithms, and unlocking industry-wide insights — internally, or collaboratively with partners.

Statice provides a synthetic data generation software that allows you or your partners to handle data more freely and efficiently. You can easily share and collaborate over sensitive and private customer data without ever exposing it.