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World desk5 min

How to Share Safety Research Data With External Partners Without Exposing Sensitive Information

Before sharing safety research data, define the partner’s authorized purpose, reduce unnecessary detail, assess residual identification risk, and select access controls and written terms that fit the project.
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Share only the data a partner is authorized to use and needs for a defined purpose. Before sending anything, check the project’s consent and other restrictions, assess identification and group-harm risks, then choose an access model and written controls that fit the remaining risk. De-identification can reduce exposure, but it cannot guarantee that people or groups will not be identified.

1. Define the purpose and authority to share

Specify what the partner needs

Write down the partner’s research question, the variables needed to answer it, who will use the data, what outputs they expect, and how long they need access. Ask whether they need row-level data at all: summaries, a narrower extract, or analysis performed by your team may answer the question with less exposure.

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Check what permits and limits the sharing

Review participant consent, ethics or institutional review conditions, applicable law, funder and repository rules, institutional policy, and any existing agreement. These requirements vary by project and jurisdiction. A dataset being technically de-identified does not by itself establish that you have authority to share it or that the proposed use is permitted.

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NIH guidance for human-participant data identifies privacy or safety risks, consent limits, and legal or policy restrictions as reasons that may justify limiting sharing. Its requirements apply in their relevant NIH context; they are not a universal rule for every safety study or partnership.

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2. Minimize the data and assess what could still identify someone

Remove unnecessary detail

Include only fields needed for the approved purpose. Review direct identifiers as well as details that could identify someone when combined: rare characteristics, precise locations or dates, small-group attributes, free-text descriptions, images, genomic data, or sensor records. Qualitative material can be particularly difficult to scrub because a narrative may identify someone through context even after names are removed.

Consider whether a less detailed version will preserve the analysis: for example, broader geographic categories, less precise dates, grouped categories, or a reduced set of variables. These changes can reduce utility, so assess them against the partner’s actual question rather than applying them mechanically.

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Consider linkage, inference, and group harms

Information that appears anonymous on its own may become identifying when combined with outside data or contextual knowledge. A combination of attributes can point to an individual, and revealing a rare characteristic may expose or stigmatize a small community even if no person is named. NIH recommends de-identifying data to the greatest extent that preserves sufficient scientific utility, while warning that outside information can enable identity inferences.

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Record what you removed, generalized, or retained, why those choices preserve the intended analysis, and what residual risks remain. Explain those limits to the partner; do not describe the data as risk-free merely because direct identifiers have been removed.

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3. Choose an access model that matches the remaining risk

Open release, controlled repository access, and analysis in an enclave are alternatives to assess—not a universal ranking. Compare them using the partner’s need for detailed data, residual identification and group-harm risks, the ability to restrict users and purposes, controls on copying or exporting results, administrative burden and access delay, partner capability, and applicable consent and jurisdictional requirements.

Access model How it works When to consider it Trade-off to assess
Open or broadly accessible release Data are available to a broad audience, with few or no individual access decisions. Only when consent and applicable review permit broad release and residual risks are acceptably low. Once distributed, data may be copied, combined with other information, or used beyond the original audience; there is limited ability to constrain individual use.
Controlled-access repository Eligible users request access and agree to conditions; an authorized body reviews requests. When data can be shared for specified purposes but sensitivity, consent, or residual risk calls for user and purpose restrictions. Review and administration can delay access; restrictions and user eligibility need to be clear and workable.
Secure enclave or data environment Approved users analyze data in a secure environment rather than receiving unrestricted copies; outputs may be subject to review. When detailed data are needed for analysis but distributing copies would create unacceptable risk or conflict with restrictions. Access depends on the environment’s security, user controls, export checks, and the partner’s ability to work within it.

NIH describes enclaves as secure environments for eligible researchers to analyze restricted or controlled resources. UK Department of Health and Social Care guidance describes secure environments for NHS health and social care data that apply minimisation and de-identification and check external inputs. These are jurisdiction- and context-specific examples, not certifications or guarantees for another project. Confirm how the environment handles access, exports, and outputs under the rules that govern your data.

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4. Put partner responsibilities in writing

Use a data-sharing or data-use agreement suited to the project. NIH recommends agreements that clearly delineate responsibilities, privacy duties, and restrictions. The agreement should align with the approved purpose and any consent, institutional, legal, or repository conditions.

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  • Purpose and scope: State the permitted use, approved data, named users or roles, and any limits on analyses or outputs.
  • Confidentiality and security: Set user access, safeguards, training or confidentiality obligations, and the process for reporting suspected incidents.
  • Copying and onward sharing: Specify whether data may be downloaded, copied, retained in backups, or shared with another organization, and under what conditions.
  • Retention and deletion: State how long access lasts and what happens to data and copies at the end of the approved period.
  • Re-identification and contact: Prohibit attempts to identify or recontact participants unless the project has explicitly authorized that activity.
  • Outputs: Where appropriate, define checks for disclosures in publications, tables, or other outputs before release.

Tell the partner what de-identification changes were made and what limitations remain, so they can interpret the data without mistaking reduced identifiers for zero risk.

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5. Keep the decision reviewable and revisit it when circumstances change

Maintain a record of the purpose, requested fields, risk assessment, access-model decision, approvals, agreement, and any output checks. Reassess if the partner, intended use, dataset, potential linkage sources, or governing requirements change. NIH encourages privacy planning early in research, but that guidance does not replace applicable law or institutional review.

For health-related research data collected under WHO programmes, WHO’s 2022 policy and implementation guidance provides a separate programme-specific context. It does not establish a blanket permission to share all safety research data. For any project, consult the responsible institutional privacy, security, ethics, and legal reviewers about its actual obligations.

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