Category: Privacy & Data Protection
Homomorphic Encryption
Also known as: HE, Fully Homomorphic Encryption (FHE)
Simply put
Homomorphic encryption is a way of scrambling data so it stays protected even while it is being used, not just while it is stored or sent. It allows calculations to be performed directly on the encrypted data without first unscrambling it, so the underlying information is not exposed during processing. This makes it possible to analyze or compute on sensitive data in environments that are not fully trusted.
Formal definition
Homomorphic encryption is a form of encryption that permits computations to be carried out directly on ciphertext, producing an encrypted result that, when decrypted, corresponds to the outcome of the operations as if they had been performed on the plaintext. As commonly described, it keeps data encrypted during storage, transfer, and processing, distinguishing it from conventional schemes that require decryption before computation. Fully homomorphic encryption (FHE) refers to schemes supporting a broad range of computations on encrypted data and is frequently positioned as a mechanism for enabling work on untrusted domains. Note that the evidence provided does not detail specific schemes, performance characteristics, or supported operation types (for example, partially versus fully homomorphic variants beyond the FHE reference), so those distinctions are out of scope here.
Why it matters
Homomorphic encryption addresses a persistent gap in data protection: most conventional encryption safeguards data at rest (in storage) and in transit (during transfer), but requires decryption before the data can be processed or analyzed. That decryption step creates a window of exposure, particularly when computation happens in environments that are not fully trusted. By allowing computations to be carried out directly on ciphertext, homomorphic encryption is positioned as a way to keep data protected across storage, transfer, and processing, reducing the points at which sensitive information is exposed.
Who it's relevant to
Privacy and data protection officers
Professionals responsible for safeguarding sensitive data may consider homomorphic encryption as a privacy-enhancing technology that extends protection into the processing stage, not only storage and transfer. It is a measure that can reduce exposure of underlying data during computation; it does not, on its own, eliminate all privacy or security risk, and its suitability depends on the specific use case and implementation.
Security architects and engineers
Those designing systems that process sensitive data in untrusted or shared environments may evaluate homomorphic encryption, including FHE, as a mechanism for performing computations without decrypting data. The evidence here does not cover performance trade-offs or supported operation types, so architects should assess these factors against detailed technical documentation before adoption.
Compliance and risk professionals
Individuals assessing controls around sensitive data handling may treat homomorphic encryption as one option within a broader set of privacy-enhancing technologies. It is relevant where reducing data exposure during processing is a control objective, but claims about its protection should be scoped to what the chosen implementation actually supports rather than assumed to be comprehensive.
Inside HE
Encryption over ciphertext computation
Homomorphic encryption (HE) is a family of encryption schemes that allow certain computations to be performed directly on encrypted data (ciphertext), producing an encrypted result that, when decrypted, matches the outcome of the same operations performed on the plaintext. The distinguishing property is that data need not be decrypted to be processed.
Scheme categories by supported operations
HE is commonly categorized by the range of computations supported. Partially homomorphic encryption typically supports one type of operation (for example, addition or multiplication) an unlimited number of times. Somewhat homomorphic encryption supports a limited number of operations of more than one type. Fully homomorphic encryption (FHE) is designed to support arbitrary computations, though at substantial computational cost as commonly implemented.
Noise growth and bootstrapping
Many HE schemes introduce noise into ciphertexts that grows as operations accumulate; excessive noise can make correct decryption impossible. Bootstrapping is a technique commonly associated with fully homomorphic schemes that refreshes ciphertexts to control noise and enable further computation, typically at significant performance expense.
Relationship to AI/ML workflows
In AI contexts, HE is often discussed as a privacy-enhancing technique that could allow model inference, or in some cases training, on encrypted inputs so that the party performing computation does not observe the underlying data. Its practical applicability in machine learning depends heavily on model complexity, latency requirements, and the operations the scheme supports.
Role among privacy-enhancing technologies
HE is one of several privacy-enhancing technologies and is frequently compared with alternatives such as secure multi-party computation, differential privacy, and trusted execution environments, each addressing different threat models and offering different performance and confidentiality trade-offs.
Common questions
Answers to the questions practitioners most commonly ask about HE.
Does homomorphic encryption mean data is fully protected throughout an AI pipeline, so no other governance controls are needed?
No. Homomorphic encryption addresses confidentiality of data during computation, but it does not by itself satisfy broader governance obligations such as access control at the endpoints, key management, audit logging, or model risk oversight. As commonly framed, it is one technical control that reduces certain exposure risks rather than a comprehensive safeguard, and it does not eliminate risk. Data typically remains vulnerable at points where it is decrypted, and organizational accountability structures still apply.
Is homomorphic encryption the same as, or interchangeable with, other privacy-enhancing techniques like secure multiparty computation, federated learning, or differential privacy?
No. These are distinct techniques that are sometimes combined but should not be conflated. Homomorphic encryption allows computation on encrypted data; secure multiparty computation distributes computation across parties without any single party seeing the full inputs; federated learning trains across decentralized data without centralizing the raw data; and differential privacy adds calibrated noise to limit inference about individuals. Each addresses different threat models and has different performance and assurance properties, so they are not substitutes for one another.
How does homomorphic encryption typically affect computational performance and cost in practice?
Homomorphic encryption is commonly associated with substantial computational and memory overhead relative to computation on plaintext, which can affect latency, throughput, and infrastructure cost. The degree of overhead varies with the scheme chosen and the operations performed. Implementers typically weigh these performance trade-offs against the confidentiality benefit for a given use case, rather than assuming it can be applied uniformly at scale.
What operational considerations arise around key management when deploying homomorphic encryption?
Because the security of the encrypted data depends on the encryption keys, key generation, storage, rotation, and access governance are typically central operational concerns. The point at which results are decrypted, and who controls the decryption keys, often determines the actual confidentiality boundary. These considerations generally fall within an organization's broader security and governance controls rather than being resolved by the encryption scheme alone.
Where in an AI workflow is homomorphic encryption most commonly considered?
It is often considered where computation must be performed on sensitive data by a party that should not have access to the underlying plaintext, such as outsourced or third-party processing scenarios. Whether it is appropriate depends on the specific operations required, tolerance for performance overhead, and the applicable data protection or sector-specific requirements. It may be more suitable for some operations than others, so scoping to the specific workflow is important.
How should the use of homomorphic encryption be documented and validated within a model risk or governance framework?
As with other technical controls, its use is typically documented as part of a system's control environment, with the threat model, assumptions, key management approach, and performance trade-offs recorded. Verifying that an implementation behaves as intended (verification) and assessing whether it is appropriate for the intended purpose and risk context (validation) are distinct activities that should not be blurred. The specific documentation and assurance expectations vary by jurisdiction, sector, and applicable framework.
Common misconceptions
Homomorphic encryption makes data private and secure end-to-end, eliminating data protection risk.
HE addresses confidentiality of data during computation for specific threat models; it does not by itself eliminate risk. Key management, access controls, decryption endpoints, correctness of the computation, and organizational governance remain sources of risk. As with other controls, HE reduces or manages certain risks rather than removing them entirely.
Fully homomorphic encryption is a practical, drop-in solution for most AI training and inference today.
Fully homomorphic schemes are typically associated with substantial computational and performance overhead, and their feasibility depends on the operations required, model complexity, and latency constraints. Whether HE is practical for a given AI workload is highly context-dependent and should not be assumed.
All homomorphic encryption schemes can compute anything on encrypted data.
Only schemes designed as fully homomorphic aim to support arbitrary computation. Partially and somewhat homomorphic schemes support limited operations or a limited number of operations, so the scheme category constrains what computations are possible.
Best practices
Match the HE scheme category (partially, somewhat, or fully homomorphic) to the specific computations required by the use case, rather than assuming a single scheme fits all workloads.
Assess performance, latency, and cost trade-offs against alternative privacy-enhancing technologies such as secure multi-party computation, differential privacy, or trusted execution environments before committing to HE.
Define the threat model explicitly, clarifying which party is untrusted and what confidentiality property HE is intended to provide, so the control is not credited with protections it does not deliver.
Maintain robust key management and secure handling of decryption endpoints, since these remain critical risk points even when computation occurs on ciphertext.
Account for noise growth and, where fully homomorphic schemes are used, the performance implications of bootstrapping when planning computations.
Document HE within broader AI governance and model risk management processes, treating it as a risk-reducing control that must be validated and monitored rather than as a standalone guarantee of privacy or security.