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.
"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.
In the previous blog we talked about the guarantees that we can provide when a user uses our confidential computing platform Avato. We touched upon how these guarantees are provided on a high level, and what this means for the data analytics industry.
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.
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