The full benefits of AI cannot be achieved until AI can be fully trusted–secure, compliant, and privacy preserving. Trusted AI requires integrating privacy-preserving verification software throughout the AI tech stack. The best approach for making secure, compliant, and privacy preserving are zero knowledge proofs. ZKPs are privacy-preserving protocols that

At ICME, we've been diligently developing a zkVM. Our target platform is WASM due to its portability and extensive language support. Our VM is designed to operate efficiently on constrained devices, such as browsers, and cater to a variety of use-cases, including zkML, web3 infrastructure, and even web2.

The term 'cycles of elliptic curves' has been garnering significant attention recently. This surge in interest is largely due to their application in 'incremental verifiable computation', the folding scheme, and more broadly, SNARK recursion. Often, when a SNARK needs to self-verify, it is typically implemented as

Occasionally, I like to envision the 'best' possible outcome and assess how close we are to it. Zero-knowledge proofs, as they stand today, are complex and difficult to review. The ideal outcome would be a simpler system that is easier to scrutinize and far more efficient than our

Floating-point numbers play a vital role in various fields, including artificial intelligence and scientific research, as they enable the accurate representation and manipulation of real numbers. However, when it comes to zero-knowledge virtual machines or domain-specific languages (DSLs), their direct implementation has yet to be created. In numerous instances, floating-point

The world of cryptography has made significant strides in the past few years, driven by the increasing need for privacy and scalability in blockchain applications. Two critical cryptographic tools that have gained significant research attention are Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE). With FHE you can perform computations

What is it and why is it so cool? You can now do computations with encrypted data Fully Homomorphic Encryption (FHE) enables computation on encrypted data. A user can take plaintext data, encrypt it, and allow that data to be sent to third parties for processing knowing that it is