Back
What is Homomorphic Encryption? | IBM
webRelevant to AI safety discussions around privacy-preserving ML and secure computation; homomorphic encryption is increasingly proposed as a tool for enabling auditable, privacy-respecting AI systems without exposing sensitive training data or model internals.
Metadata
Importance: 42/100blog posteducational
Summary
IBM's explainer on homomorphic encryption (HE), a cryptographic technique that allows computations to be performed on encrypted data without decrypting it first. It covers how HE works, its types (partial, somewhat, fully), and its potential applications in privacy-preserving AI and data processing. The resource highlights HE as a key enabling technology for secure, privacy-respecting machine learning and cloud computing.
Key Points
- •Homomorphic encryption allows computation on ciphertext, producing encrypted results that match operations performed on plaintext.
- •Fully homomorphic encryption (FHE) supports arbitrary computations but remains computationally expensive; partial and somewhat HE offer practical tradeoffs.
- •HE enables privacy-preserving AI: models can be trained or run inference on sensitive data without exposing raw information.
- •Key use cases include secure cloud computing, healthcare data analysis, and financial data processing under regulatory constraints.
- •IBM is an active developer of HE toolkits (e.g., HElib), positioning the technology as critical infrastructure for future AI safety and privacy.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Driven Concentration of Power | Risk | 65.0 |
Cached Content Preview
HTTP 200Fetched Apr 10, 202610 KB
What is Homomorphic Encryption? | IBM
What is homomorphic encryption?
What is homomorphic encryption?
Fully homomorphic encryption (FHE) is an innovative technology that can help you achieve zero trust by unlocking the value of data on untrusted domains without needing to decrypt it.
Today’s business data is stored across hybrid multicloud environments, exposing it to various security and privacy risks. While encryption provides protection, the sensitive data typically must first be decrypted to access it for computing and business-critical operations.
This opens the door to potential compromise of privacy and confidentiality controls. Until now, those vulnerabilities have been the cost of doing business in the cloud and with third parties.
With fully homomorphic encryption, you can better enforce zero trust because the data is always encrypted and can be shared, even on untrusted domains in the cloud, while remaining unreadable by those doing the computation.
In short, one can now do high-value analytics and data processing, by internal or external parties, without requiring that data to be exposed.
The latest tech news, backed by expert insights
Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement .
Thank you! You are subscribed.
Benefits of homomorphic encryption
Gain valuable insights
Generate measurable economic benefits by allowing lines of business and third parties to perform big data analytics on encrypted data while maintaining privacy and compliance controls.
Collaborate confidently on hybrid cloud
Process encrypted data in
... (truncated, 10 KB total)Resource ID:
fd0fccf409c94f3e | Stable ID: sid_p0xVv9yWCR