privacy-preserving inference

AntTech

Feb 19, 2025 · Information Security

Rondo and BumbleBee: Scalable Randomness Beacon and Secure Two‑Party Inference for Large Transformers

This article introduces two Ant Group papers selected for NDSS 2025—Rondo, a scalable, reconfiguration‑friendly distributed randomness beacon for blockchain‑type systems, and BumbleBee, a communication‑efficient two‑party inference framework that protects privacy for large Transformer models.

BlockchainRandomness Beacondistributed systems

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Rondo and BumbleBee: Scalable Randomness Beacon and Secure Two‑Party Inference for Large Transformers

AntTech

Feb 12, 2025 · Information Security

Selected Ant Group Papers Presented at NDSS 2025

The 2025 NDSS conference in San Diego featured five Ant Group papers covering secure forensics for compromised TrustZone, privacy‑preserving inference for large Transformers, LLM‑driven shell command explanation, a scalable randomness beacon protocol, and enclave construction within confidential virtual machines.

NDSSRandomness BeaconSecurity

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Selected Ant Group Papers Presented at NDSS 2025

AntTech

Dec 6, 2024 · Artificial Intelligence

Nimbus: Secure and Efficient Two‑Party Inference for Transformers

The paper introduces Nimbus, a two‑party privacy‑preserving inference framework for Transformer models that leverages a client‑side outer‑product linear‑layer protocol and distribution‑aware polynomial approximations for non‑linear layers, achieving up to five‑fold speedups with negligible accuracy loss.

Performance OptimizationTransformerhomomorphic encryption

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Nimbus: Secure and Efficient Two‑Party Inference for Transformers