With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it.
With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it.
With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale is the enormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvements. HEAX leverages multiple levels of parallelism, ranging from ciphertext-level to fine-grained modular arithmetic level. Our first contribution is a new highly-parallelizable architecture for number-theoretic transform (NTT) which can be of independent interest as NTT is frequently used in many lattice-based cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. Our implementation on reconfigurable hardware demonstrates 164-268× performance improvement for a wide range of FHE parameters.
If you’ve ever watched a toddler with a toy attempt to share then you know how hard it is to teach and create a culture of sharing.
Pyte (fka CipherMode) launches SecureMatch, the only data collaboration solution that allows full computation on encrypted customer data without the need for decryption at any point in the data lifecycle.
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Learn about the importance of "data clean rooms" - a solution that allows for private data sharing and analysis - in this blog post. We discuss the trade-offs involved in choosing between generic third-party solutions and custom open-source options, and how open-source SMC frameworks provide building blocks for custom clean rooms. With open SMC technologies and the right team, any company can achieve a strategic advantage through proprietary, self-governed data analysis and sharing systems that move as fast as - or faster than - vendor options ever will. Join us in exploring the future of data privacy and innovation on companies' terms.
