To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. Confidential computing focuses on potentially revolutionary technology, in terms of impact on data security. In confidential computing, data remains encrypted, not just at rest and in transit, but also in use, allowing analytics and machine learning (ML) to be performed on the data, while maintaining its confidentiality. The capability to encrypt data in use opens up a massive range of possible real-world scenarios, and it has major implications and potential benefits for the future of data security. VentureBeat spoke with Raluca Ada Popa about her research and work in developing practical solutions for confidential computing.

Automatically identifying implicit discourse relations requires an in-depth semantic understanding of the text fragments involved in such relations. While early work investigated the usefulness of different classes of input features, current state-of-the-art models mostly rely on standard pre-trained word embeddings to model the arguments of a discourse relation. In this paper, we introduce a method to compute contextualized representations of words, leveraging information from the sentence dependency parse, to improve argument representation.

Opaque, a new startup born out of Berkely's RISELabs, announced a $9.5 million seed round today to build a solution to access and work with sensitive data in the cloud in a secure way, even with multiple organizations involved. Intel Capital led today's investment with participation by Race Capital, The House Fund and FactoryHQ. The company helps customers work with secure data in the cloud while making sure the data they are working on is not being exposed to cloud providers, other research participants or anyone else, says company president Raluca Ada Popa. "What we do is we use this very exciting hardware mechanism called Enclave, which [operates] deep down in the processor -- it's a physical black box -- and only gets decrypted there. Company co-founder Ion Stoica, who was a co-founder at Databricks, says the startup's solution helps resolve two conflicting trends. On one hand, businesses increasingly want to make use of data, but at the same time are seeing a growing trend toward privacy. Opaque is designed to resolve this by giving customers access to their data in a safe and fully encrypted way. Data is the world's most valuable (and vulnerable) resource The company describes the solution as "a novel combination of two key technologies layered on top of state-of-the-art cloud security--secure hardware enclaves and cryptographic fortification." This enables customers to work with data -- for example to build machine learning models -- without exposing the data to others, yet while generating meaningful results. Popa says this could be helpful for hospitals working together on cancer research, who want to find better treatment options without exposing a given hospital's patient data to other hospitals, or banks looking for money laundering without exposing customer data to other banks, as a couple of examples. Investors were likely attracted to the pedigree of Popa, a computer security and applied crypto professor at UC Berkeley and Stoica, who is also a Berkeley professor and co-founded Databricks. Both helped found RISELabs at Berkeley where they developed the solution and spun it out as a company. Mark Rostick, vice president and senior managing director at lead investor Intel Capital says his firm has been working with the founders since the startup's earliest days, recognizing the potential of this solution to help companies find complex solutions even when there are multiple organizations involved sharing sensitive data. "Enterprises struggle to find value in data across silos due to confidentiality and other concerns.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Opaque, a startup that helps organizations analyze encrypted data in the cloud, today announced that it closed a $9.5 million seed funding round led by Intel Capital with contributions from Race Capital, The House Fund, and FactoryHQ. Cofounder Raluca Ada Popa says that the funds will help to expand Opaque's ongoing contributions to the open source and data security communities. A majority of data sits in private hands.

Identifying or authenticating people based on how they type is not a new idea, but thanks to advances in artificial intelligence it can now be done with a very high level of accuracy, making it a viable replacement for other forms of biometrics. Research in the field of keystroke dynamics, also known as keyboard or typing biometrics, spans back over 20 years. The technique has already been used for various applications that need to differentiate among computer users, but its widespread adoption as a method of authentication has been held back by insufficient levels of accuracy. Keystroke dynamics relies on unique patterns derived from the timing between key presses and releases during a person's normal keyboard use. The accuracy for matching such typing-based "fingerprints" to individual persons by using traditional statistical analysis and mathematical equations varies around 60 percent to 70 percent, according to Raul Popa, CEO and data scientist at Romanian startup firm TypingDNA.

Identifying or authenticating people based on how they type is not a new idea, but thanks to advances in artificial intelligence it can now be done with a very high level of accuracy, making it a viable replacement for other forms of biometrics. Research in the field of keystroke dynamics, also known as keyboard or typing biometrics, spans back over 20 years. The technique has already been used for various applications that need to differentiate among computer users, but its widespread adoption as a method of authentication has been held back by insufficient levels of accuracy. Keystroke dynamics relies on unique patterns derived from the timing between key presses and releases during a person's normal keyboard use. The accuracy for matching such typing-based "fingerprints" to individual persons by using traditional statistical analysis and mathematical equations varies around 60 percent to 70 percent, according to Raul Popa, CEO and data scientist at Romanian startup firm TypingDNA.

Identifying or authenticating people based on how they type is not a new idea, but thanks to advances in artificial intelligence it can now be done with a very high level of accuracy, making it a viable replacement for other forms of biometrics. Research in the field of keystroke dynamics, also known as keyboard or typing biometrics, spans back over 20 years. The technique has already been used for various applications that need to differentiate among computer users, but its widespread adoption as a method of authentication has been held back by insufficient levels of accuracy. Keystroke dynamics relies on unique patterns derived from the timing between key presses and releases during a person's normal keyboard use. The accuracy for matching such typing-based "fingerprints" to individual persons by using traditional statistical analysis and mathematical equations varies around 60 percent to 70 percent, according to Raul Popa, CEO and data scientist at Romanian startup firm TypingDNA.