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Private Inference

Definition ∞ Private inference is a cryptographic technique that allows a user to query a machine learning model without revealing their input data to the model owner, and conversely, without revealing the model parameters to the user. This method ensures confidentiality for both the query and the model during the prediction process. It utilizes advanced cryptographic primitives such as homomorphic encryption or secure multi-party computation. The objective is to enable secure, privacy-preserving use of AI services.
Context ∞ In the digital asset and blockchain domain, private inference holds considerable promise for secure identity verification, credit scoring, and personalized financial services without compromising user data. News may discuss its potential application in decentralized identity solutions or confidential smart contracts that require AI-driven decisions. This technology addresses the tension between leveraging AI for utility and maintaining stringent data privacy in transparent or pseudonymous environments.

This macro view showcases a sophisticated piece of hardware, likely a specialized chip or module, rendered in metallic blue and silver tones. Its intricate design features raised metallic textures and recessed ports, suggesting complex data pathways and secure data enclave functionalities. The sharp, geometric lines and the dark, reflective screen-like surface evoke concepts of secure digital asset management and the physical infrastructure underpinning decentralized ledger technologies, such as private key storage or node hardware. It represents the tangible intersection of cryptography and distributed systems.

→Cryptographic Protocols

→Secure Multi-Party Computation

→Backdoor Attacks

GuardianMPC: Backdoor-Resilient Neural Network Computation via Secure MPC

A novel framework leverages secure multi-party computation to protect neural networks from backdoor attacks, ensuring private, robust AI inference and training.

Private Inference

ZKTorch: Efficient, Private ML Inference via Parallel Zero-Knowledge Proof Accumulation

GuardianMPC: Backdoor-Resilient Neural Network Computation via Secure MPC

Incrypthos

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