Oblivious offers advanced privacy-enhancing technologies that allow organizations to process sensitive data securely without compromising individual privacy. Their key solutions include OBLV Deploy, which runs applications in a secure environment, and AGENT, a platform for analyzing sensitive data while protecting individual privacy. With a strong focus on integrating seamlessly into existing workflows, Oblivious aims to empower organizations to maximize their data potential while maintaining robust security, transparency, and compliance with privacy laws.
• seamless integration with existing workflows
• compliance with privacy regulations
• secure application environment (oblv deploy)
• detailed data analysis (agent)
• privacy protection for sensitive information
• advanced security features including secure enclaves
• community engagement events and competitions
AGENT is for organisations who want to enable data scientists and machine learning models to work and collaborate on sensitive data without compromising individual privacy.
Our solutions are built for any company that handles sensitive data and aims to strengthen its data privacy and data security. They also provide unique data collaboration opportunities within and between organisations that wish to keep their sensitive data and IP private.
Yes, our solutions are designed to cater to a wide range of users. They're capable of handling both highly sensitive data and high volumes, providing a robust and scalable solution for businesses of varying sizes and needs.
Absolutely, we are committed to continuous innovation. As privacy laws and regulations evolve, we continually refine our products to ensure they not only adhere to but also preemptively address these advancements, guaranteeing seamless governance and compliance for your business.
Confidential computing is a security approach focusing on protecting data while it is being processed. Unlike traditional security measures that secure data at rest and in transit, confidential computing closes the gap by safeguarding data during use.
Differential privacy is a framework designed to ensure individual data remains private when conducting statistical analyses. It achieves this by introducing controlled random noise into data queries.
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