DataTerminology.comNEW
A cryptographic method enabling computation on encrypted data, producing results that decrypt to the correct plaintext computation.
DESCRIPTION
Homomorphic encryption allows useful computation to be performed directly on ciphertexts, so results decrypt to the same outcome as if operations ran on plaintext. It underpins privacy-preserving analytics, model inference, and data sharing across untrusted boundaries. Fully homomorphic encryption supports arbitrary computations; partially or somewhat homomorphic schemes support limited operations. Practical adoption hinges on performance, parameter selection, and security assumptions (often lattice-based). Emerging libraries and hardware acceleration are reducing overheads, enabling confidential ML, regulated data collaboration, and secure cloud processing.
ALTERNATIVE DEFINITIONS
Wikipedia
Homomorphic encryption is a form of encryption that allows computation on ciphertexts, generating an encrypted result which, when decrypted, matches the result of operations performed on the plaintext.
Highlights the central property of matching plaintext operations after decryption.
IBM
Homomorphic encryption is a form of encryption that allows users to perform computations on encrypted data without first decrypting it.
Emphasizes performing computation without decryption, aligning with enterprise use cases.
HomomorphicEncryption.org
Homomorphic encryption allows computation on encrypted data.
Concise standardization-oriented statement from the field’s consortium.
RELATED TERMS
Fully Homomorphic Encryption Partially Homomorphic Encryption Federated Learning Zero-Knowledge Proof Asymmetric Encryption Lattice-Based Cryptography Oblivious Transfer
COMPETING TERMS
Secure Multiparty Computation Trusted Execution Environment Differential Privacy
RELATIONS
- Broader: Encryption
KNOWLEDGE AREAS
Data Security Data Privacy & Compliance AI Data Management
TARGET AUDIENCE
AI Engineer Data Engineer MLOps Engineer Data Architect Cloud Architect Data Governance Manager Chief Technology Officer
EXTERNAL SOURCES
- Wikipedia - Homomorphic encryption
- Microsoft SEAL - Widely used homomorphic encryption library docs
- OpenFHE - Open-source FHE library and docs
- HomomorphicEncryption.org - Parameters and security standardization effort
TOP QUESTIONS
Why is Homomorphic Encryption relevant for AI workflows?
It enables inference and analytics on encrypted data, letting teams use sensitive information without exposing it, which is crucial for regulated industries and cross-organization collaboration.
Category: Relevancy
What is the difference between Fully and Partially Homomorphic Encryption?
Fully homomorphic encryption supports arbitrary computations; partially or somewhat homomorphic schemes support limited operations like addition or multiplication only.
Category: Example
What are typical use cases of Homomorphic Encryption?
Secure cloud analytics, confidential ML inference, privacy-preserving data sharing, and outsourced computation are common scenarios.
Category: Example
What are key performance challenges in Homomorphic Encryption?
It introduces significant computational overhead; careful parameter tuning, batching techniques, and modern libraries help mitigate latency and memory costs.
Category: Risks
How does Homomorphic Encryption compare to Secure Multi-Party Computation?
Both enable privacy-preserving computation; HE uses ciphertext processing by one party, while SMPC splits computation across parties without revealing inputs.
Category: Alternatives
What cryptographic assumptions underlie Homomorphic Encryption?
Most practical schemes rely on lattice-based assumptions, such as Learning With Errors, offering post-quantum security potential.
Category: Requirements
Can Homomorphic Encryption be used with federated learning?
Yes, it can encrypt gradients or updates in federated learning, enhancing privacy beyond aggregation techniques alone.
Category: Example
What is the biggest misunderstanding about Homomorphic Encryption?
People often assume it’s plug-and-play; in reality, scheme selection, parameters, and algorithm redesign are needed for practical performance.
Category: Misunderstanding
How should teams start applying Homomorphic Encryption?
Begin with well-supported libraries like SEAL or OpenFHE, prototype targeted workloads, and benchmark against privacy alternatives and business constraints.
Category: HowTo