arXiv:2605.23096v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computation to a third party server without ever revealing the user's data. Currently, the CKKS scheme is the backbone of most efficient FHE implementations, but it only supports addition, multiplication, and array rotation operations, thus requiring all activation functions of the neural network to be approximated by polynomials within a certain interval, imposing strict design tolerances. In this paper
The increasing deployment of AI and the growing demand for data privacy make the problem of secure, private inference in neural networks a critical, current research area.
This research addresses a fundamental limitation in fully homomorphic encryption (FHE) for AI, potentially enabling more robust and practical private AI solutions across various sectors.
The development of encrypted neural networks without overflows improves the reliability and practicality of FHE for complex AI models, broadening its applicability for sensitive data.
- · Privacy-focused AI companies
- · Healthcare sector (data privacy)
- · Financial sector (secure transactions)
- · Cloud computing providers (secure AI as a service)
- · Entities reliant on clear-text data processing for AI
- · Attackers targeting data in transit/at rest
- · Less efficient FHE methods
More widespread adoption of privacy-preserving AI inference for sensitive applications.
Reduced regulatory hurdles and increased public trust in AI systems handling personal or proprietary data.
The acceleration of AI as a service, allowing for computation on encrypted data without ever exposing it to the service provider.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG