Homomorphic encryption allows computations to be performed on encrypted data without decrypting it, preserving confidentiality. However, its adoption faces significant challenges due to performance overhead. According to the CompTIA SecurityX CAS-005 study materials (Domain 3: Cybersecurity Technology, 3.3), homomorphic encryption requires substantial computational resources, which standard processors struggle to provide efficiently. Specialized hardware, such as coprocessors (e.g., GPUs or TPUs), is oftenneeded to handle the complex mathematical operations involved. The lack of widespread, optimized coprocessor support in existing infrastructure is a primary barrier to adoption.
Option A (Incomplete mathematical primitives):While early homomorphic encryption schemes had limitations, modern schemes (e.g., CKKS, BFV) have mature mathematical foundations, making this less of a challenge today.
Option B (No use cases):Use cases exist, such as secure cloud computing and privacy-preserving data analytics, so this is not accurate.
Option C (Quantum computers):Homomorphic encryption is not dependent on quantum computing, and quantum computers are unrelated to its current challenges.
Option D (Insufficient coprocessor support):This is the most accurate, as performance bottlenecks require specialized hardware that is not yet widely available or integrated.
[Reference:, CompTIA SecurityX CAS-005 Official Study Guide, Domain 3: Cybersecurity Technology, Section 3.3: "Evaluate emerging cryptographic technologies, including homomorphic encryption challenges.", CAS-005 Exam Objectives, 3.3: "Analyze barriers to adopting advanced encryption techniques.", , , , ]
