AI Security Hype vs Reality: The Year Attackers Got Smarter For years, AI in cybersecurity has been more theater than substance. Dashboards that promised “self-learning defense.” Vendors who sold Bayesian math as “machine intelligence.” Slide decks louder than their code. Then came November 30, 2022. The day language models learned to think in sentences—and the…
Trusted Execution Environments (TEEs) were once the crown jewel of confidential computing. Intel SGX, AMD SEV, and ARM TrustZone promised to protect data during processing by isolating it inside secure hardware enclaves. For years, this model has powered many things from cloud-based machine learning to fintech data analytics. But recent months have delivered a wake-up…
We’re excited to announce our partnership with Tunnel ID, bringing together two breakthrough technologies to create the most secure, private, and user-friendly identity verification solution. The Challenge We’re Solving Together Identity fraud has reached crisis levels globally, with cybercrime costs hitting $9.5 trillion worldwide in 2024. Deepfake fraud attempts have exploded by 2,137% over the…
A pager goes off in the cardiac unit at 01:37. A nurse has to send a CT scan to the triage model before the surgeon wheels a patient into theatre. In most hospitals that file leaves its secure folder, lands on a server, runs through the model in plain text, then returns with a prediction.…
Why FHE in Federated Learning?
1. Enhanced Privacy:
FL already ensures data stays on the client side, but transmitting gradients or model updates to a central server can still leak sensitive information through inference attacks.
FHE adds an extra layer of security by ensuring that even the server cannot access the raw gradients or updates—it only processes encrypted data.
Homomorphic encryption is a cutting-edge cryptographic technique that allows computations on encrypted data without decryption, ensuring the underlying information’s confidentiality.
This encryption method is classified into three main categories: Partial Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE).
Fully Homomorphic Encryption (FHE) What is Fully Homomorphic Encryption? Fully Homomorphic Encryption (FHE) is an innovative cryptographic technique that enables computations to be performed directly on encrypted data without needing to decrypt it first. This feature ensures that sensitive data remains secure throughout the computational process, significantly reducing the risk of unauthorized access. Unlike traditional…