Analyzing the feasibility of Fully Homomorphic Encryption (FHE) for large-scale deep learning models reveals that while it provides the highest level…

The statement accurately captures the fundamental trade-off of using Fully Homomorphic Encryption (FHE) in its current state. FHE's unique capability is performing computations on data while it remains encrypted, offering the strongest form of data-in-use protection. However, these cryptographic operations are vastly more complex and resource-intensive than equivalent operations on plaintext. This results in a significant performance penalty, manifesting as high computational overhead and increased latency, which is a primary barrier to its widespread adoption for large-scale, real-time AI models.

False: This is incorrect because the statement is a well-documented and accurate description of FHE's primary benefit (data-in-use security) and its most significant current drawback (performance overhead).

1. Acar, A., et al. (2018). A Survey on Homomorphic Encryption Schemes: Theory and Implementation. ACM Computing Surveys, 51(4), Article 79. Section 6. DOI: https://doi.org/10.1145/3214303. (This survey explicitly discusses the performance challenges and computational overhead as major limitations of FHE schemes).

2. Gentry, C. (2009). A Fully Homomorphic Encryption Scheme. Ph.D. dissertation, Stanford University. (The foundational work on FHE, which acknowledges the initial "prohibitively slow" performance of the first plausible scheme).

3. Boemer, F., et al. (2021). nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data. arXiv:2102.09410 [cs.CR]. (This paper details efforts to optimize FHE for neural networks, implicitly confirming the inherent performance challenges that necessitate such work).