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