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Imported item 10
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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

H

Imported item 48

Homomorphic encryption is a cryptographic approach that allows certain computations to be performed on encrypted data without first decrypting them. The encrypted result can then be decrypted by the appropriate key holder. This can reduce the need to reveal the underlying information to the party performing an agreed calculation or analysis.

Key takeaways

  • Computation can happen without exposing the input plaintext.
  • Capabilities depend on the encryption scheme.
  • Key management and output review still matter.

How the Workflow Works

The NIST overview of fully homomorphic encryption explains the broadest form, which supports arbitrary computable functions. The UK privacy-enhancing technologies awareness tool provides practical context for assessing implementation benefits and costs.

StageIllustrative action
EncryptA data holder encrypts the input.
ComputeAnother system processes the ciphertext.
DecryptAn authorised key holder recovers the result.

Why It Matters for Data Protection

Our recommendation is to assess the whole workflow, not only the encrypted calculation. Define who holds the keys, which outputs can be released and whether small groups or repeated queries could reveal sensitive information. Benchmark the intended analysis on realistic data volumes before committing to a design. A technically feasible demonstration does not establish that a production workflow meets its performance, security and governance needs.

Hypothetical example: Two research organisations want to calculate a limited aggregate statistic without sending readable source records to the computing service. They test an appropriate scheme and validate the decrypted result against a controlled reference calculation. They separately agree who may request analyses and approve output release.

Frequently Asked Questions

Is homomorphic encryption the same as ordinary encryption?

It adds supported computation over ciphertext; ordinary encrypted storage alone does not provide that capability.

Does every scheme support every calculation?

No. Fully homomorphic encryption has broader computational capability than more restricted schemes.

Does it automatically make health data anonymous?

Do not assume so. Review keys, outputs, other available information and the applicable legal test separately.

Related reading: privacy-enhancing technologies, encryption. For practical support, see clinical data hosting.

Updated: 27 October 2026