Learn why having a secure and confidential way to collaborate on data matters more than ever.
We’re proud to have been a part of the first ever summit focusing on privacy-enhancing computation solutions in early-July, and here we share our three key takeaways from the Secure and Private Compute Summit!
Market research firms face challenges to conducting competitive benchmarking studies – all of which center on participating companies’ concerns on data privacy and meeting regulatory requirements.
While today's data clean rooms facilitate analysis of sensitive and confidential data within pre-defined guidelines and privacy controls, many still do not fully address the need for increased data security and data privacy, especially in highly regulated industries.
In December 2020, SolarWinds, Inc., a leading provider of network performance monitoring tools, suffered attack from a SUNSPOT malware. It hit not just SolarWinds, but also its clients, including US government agencies and Fortune 500 companies, leaving valuable data assets exposed and compromised.
Learn how data ecosystems allow companies to enhance the sophistication of their data-driven decision making and machine learning models.
"This trend enables organizations to collaborate on research securely across regions and with competitors without sacrificing confidentiality. This approach is designed specifically for the increasing need to share data while maintaining privacy or security."
Reports and market caps show that being a data-driven company is not only paying off, but also that it is becoming the only option to navigate the data producing economy we live in. Companies understand this, so they rush to update their business models to become “data-driven” or “data-first”.
The question of data privacy in machine learning is still widely overlooked. In this blog, we propose a new machine learning training metric that greatly reduces the risk of data reidentification. It can be embedded in any model training with minimal cost.
Mergers and acquisitions (M&A) totalled in worth almost $2.49 trillion in the first three quarters of 2019. Some prominent examples are Saudi Aramco/Saudi Basic Industries Corporation, AbbVie/Allergan, Bristol-Myers Squibb/Celgene, and United Technologies/Raytheon.
What is blocking telcos from leveraging more of their networks, the generated data and machine learning? Two things: data security and data privacy. This is where confidential computing comes into play.
Combining datasets across organizations can unlock huge value, but companies are reluctant to share data due to sensitivity concerns. In this post we explore how we remove the need for a trusted third party (even Decentriq!), by using Intel® Software Guard Extensions (Intel® SGX).
People acknowledge the value of data and the benefits of data collaborations. Yet, concerns about data privacy and security are increasing. To solve these challenges, the leaders in the field of confidential computing establish new technologies to make data collaborations simple and safe.
Until today, data-sensitive companies are unable to utilize the immense benefits of cloud computing. Confidential Computing is here to change this. It is well known that cloud-computing offers many advantages, from increased scalability to reduced operations costs.
Elliptic Curve Cryptography (ECC) is a powerful tool that has many applications in the blockchain space and cryptography in general. With one of the last releases, we added support for efficient ECC-based cryptographic primitives inside a zkSNARK construction.
Buying data is a lengthy process on its own. Finding the right vendor, type of data, and passing all the regulatory and legal procedures takes time and costs money. On top of that, in practical reality, the procedure is even lengthier because it starts before the decision to buy the data.
Over the last centuries, humanity has tested various governance models for society and individuals. Checks and balances, separation of powers and democracy to be governed as fair as possible. It’s about not giving too much power to a single entity by having proper governance methods in place.
Today for most organizations it is hard to impossible to benefit from artificial intelligence (AI). Especially, when it comes to training and applying machine learning models because they require substantial investments of money, time, data and expertise.
According to the annual WEF report on global risks, increased cyber-threats is one of the biggest risks that companies and countries face right now. One of the most common is data breach where attackers target the database of the company in order to get access to customer data.
When Apple released the iPhone 5S in 2013, most people focused on its new camera and features such as Touch ID. However, on top of these features, Apple introduced the Secure Enclave Processor (SEP) as a separate sub-processor that would store sensitive data and run computer programs on top of it.
Cryptography is the backbone of our current digital society, but how did it become so important? Interestingly, the systematic study of cryptography as science started only during the past century. The first type of cryptography was writing since the majority of people could not read.
In this blog we implement a problem very typical for blockchain use-cases: proving the knowledge of a pre-image for a given SHA-256 digest. We will begin demystifying this machinery by computing the SHA-256 hash of the number 5. We will navigate through several options using different languages.
In this series of posts, we will look at ZKPs: a family of probabilistic protocols that has garnered increased popularity with the rise of distributed ledger technology (DLT). Let us introduce some of the theory behind this groundbreaking work and the components of its intricate machinery.
Watch on-demand as Stefan Deml, Co-Founder and CTO of Decentriq, joins Vikas Bhatia, Head of Product, Azure Confidential Computing at Microsoft, and Alice Dal Fuoco, Innovation Manager at PostFinance, for a discussion on how confidential computing enables data use cases previously not possible.
Watch on-demand as Maximilian Groth, Co-Founder and CEO of Decentriq, and Georges De Feu, CEO & Co-Founder of LynxCare, share how to unlock sensitive healthcare data to drive real-world evidence (RWE) collaborations between hospitals and pharmaceutical companies.
Stefan Deml, Co-Founder and CTO of Decentriq, is the guest speaker on this episode of Intel® Chip Chat. Stefan covers what Decentriq is doing to offer solutions that prioritize data safety and simplicity in a new era of confidential computing.
David Sturzenegger, Head of Product at Decentriq, is one of the panel speakers in this Science|Business online webinar, which explores how policymakers could and should shape the flow of data between countries and between the public and private actors.
As companies are becoming more and more data driven, the need for stronger privacy and confidentiality in the cloud has never been greater. Tune in on-demand for our webinar co-hosted with Intel and Swiss Re on how companies can unlock the value of private data.