1. Introduction

A data clean room (DCR) is a controlled technical and legal environment in which two or more organizations can analyze combined datasets without freely exchanging or directly accessing each other’s underlying confidential data.

In the pharmaceutical industry, data clean rooms can facilitate collaborations among drug manufacturers, hospitals, clinical research organizations (CROs), laboratories, pharmacies, health insurers, biotechnology companies, and research institutions.

For example, a pharmaceutical manufacturer and a hospital network may wish to evaluate the effectiveness of a medicine in different patient populations. A clean room may allow approved statistical analyses of clinical outcomes while restricting access to identifiable patient records.

The central legal question is whether these arrangements protect privacy and trade secrets while avoiding unlawful data processing, anticompetitive coordination, discrimination, or misuse of confidential medical information.

Key principle: A data clean room is not automatically lawful merely because the technology restricts access to raw data. Its legality depends on the purpose of processing, the nature of the data, the parties’ rights, the analytical outputs, the safeguards, and the applicable jurisdiction.

2. Meaning and operation of a pharmaceutical data clean room

Participant A: Pharmaceutical company

Clinical trial data, drug safety information, research records

Secure data clean room

Access controls, pseudonymization, approved queries, audit logs, output screening

Participant B: Hospital or research partner

Patient outcomes, laboratory results, treatment and follow-up data

Permitted outputs may include aggregated safety trends, treatment-effect estimates, or research statistics. The clean room must prevent unauthorized disclosure and re-identification to the extent required by law.

Typical safeguards include:

Data minimization: using only data necessary for the approved research or business purpose.

Pseudonymization: replacing direct identifiers with codes, while recognizing that coded data may remain personal data.

Role-based access: limiting who can access datasets, query systems, and view outputs.

Output controls: blocking small-cell results, identifiable records, and queries that could reveal individual patients.

Purpose limitation: preventing the use of research data for unrelated marketing, employee monitoring, or competitive intelligence.

Auditability: maintaining records of access, queries, exports, approvals, and incidents.

A clean room can be operated by one participant, a neutral third party, a cloud provider, or a specialized data platform. The operator’s role matters because it may itself process personal data or confidential business information.

3. Legal framework governing pharmaceutical data clean rooms

The legality of a clean room must be assessed under several overlapping legal regimes. A single collaboration may involve privacy law, pharmaceutical regulation, competition law, intellectual property rights, research ethics, and contractual confidentiality.

A. Data protection and patient confidentiality

Health and genomic information are especially sensitive because they may reveal diagnoses, genetic predispositions, treatment history, reproductive information, or other intimate details.

European Union: GDPR

The General Data Protection Regulation (GDPR) is particularly important where pharmaceutical collaborations process personal data relating to people in the EU.

Relevant provisions include:

Article 5: Lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, and security.

Article 6: A valid legal basis for processing personal data.

Article 9: Additional restrictions on processing health and genetic data, subject to specified exceptions.

Article 25: Data protection by design and by default.

Article 28: Obligations concerning processors.

Article 32: Appropriate technical and organizational security measures.

Article 35: Data protection impact assessments where processing is likely to create high risks.

Article 89: Safeguards for qualifying scientific research, including appropriate technical and organizational measures.

A clean room does not itself provide a lawful basis under Article 6 or an exception under Article 9. Both requirements must be met independently. Research organizations must also consider applicable national laws governing medical records, genetic information, and research.

Source: General Data Protection Regulation; see the text of GDPR Article 9 and the European Commission's discussion of health-data safeguards.

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United States: HIPAA and consumer protection

In the United States, the Health Insurance Portability and Accountability Act (HIPAA) may apply when covered entities or business associates use or disclose protected health information (PHI).

Relevant requirements include:

Permitted uses and disclosures under 45 C.F.R. §§ 164.502 and 164.506.

Research-related disclosures under 45 C.F.R. § 164.512(i), where applicable.

Business associate arrangements under 45 C.F.R. §§ 164.502(e) and 164.504(e).

De-identification under 45 C.F.R. § 164.514(b), using the applicable Safe Harbor or Expert Determination method.

Security safeguards under 45 C.F.R. Part 164, Subpart C.

A pharmaceutical company receiving data from a hospital must establish whether the disclosure is permitted and whether the recipient or platform provider has the required contractual status.

HIPAA is not a universal health-data law. Data outside its scope may nevertheless be subject to the FTC Act, state consumer-health privacy statutes, state medical privacy laws, or other federal requirements.

The Federal Trade Commission has expressly cautioned that data clean rooms do not automatically eliminate privacy risks or excuse unlawful data collection, disclosure, or use.

Federal Trade Commission

B. Indian law: Pharmaceutical data clean rooms

For an India-based collaboration, the following framework is particularly relevant.

1. Digital Personal Data Protection Act, 2023 (DPDP Act)

The DPDP Act establishes a framework for processing digital personal data. Relevant provisions include the requirements concerning lawful processing, notice and consent where applicable, specified legitimate uses, security safeguards, personal-data breaches, and individual rights.

However, its commencement is phased. The notification dated 13 November 2025 schedules the principal processing provisions in sections 3–17 to commence 18 months after publication, in May 2027. Therefore, as of 4 November 2026, those provisions should not be treated as already fully operative. The exact commencement of each provision must be checked against the applicable Gazette notifications.

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2. Information Technology Act, 2000 and SPDI Rules, 2011

Until the relevant transition takes effect, the existing framework remains important, particularly section 43A of the Information Technology Act and the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011, to the extent applicable.

The SPDI Rules specifically address sensitive personal data, including medical records, medical history, and certain biometric information. Their consent, disclosure, security, and transfer requirements may affect a hospital–pharma clean room.

3. Clinical trial and research requirements

A clean room used for regulated clinical research does not displace applicable requirements under the New Drugs and Clinical Trials Rules, 2019, the Drugs and Cosmetics Act, 1940, institutional ethics requirements, or applicable Indian Council of Medical Research (ICMR) ethical guidance.

The arrangement must preserve relevant obligations concerning informed consent, confidentiality, clinical-trial records, safety reporting, inspection, and regulatory access.

Practical conclusion for India: A pharmaceutical clean room may be lawful, but it should not be launched on the assumption that technical separation alone satisfies data protection or clinical research law. The precise statutory provisions in force on the date of processing must be verified.

4. Competition law: The central risk in pharmaceutical collaborations

Competition law is especially important when two pharmaceutical companies are competitors, potential competitors, or participants in the same product market.

A clean room can reduce the risk of unlawful information exchange, but it can also become a mechanism through which competitors obtain insights into one another's future commercial strategies.

Information that may be particularly sensitive

High-risk information

Future drug prices, discounts, or tender bids

Planned launch dates and output levels

Future production capacity and supply restrictions

Product-specific sales forecasts

Strategic responses to competitor launches

Potentially lower-risk information, subject to context

Aggregated adverse-event statistics

Historical epidemiological trends

De-identified research outcomes

Joint safety surveillance and scientific analyses

Aggregated real-world evidence that does not reveal commercial strategy

These categories are not absolute. Historical or aggregated data can still be sensitive in a concentrated market, particularly if the information is granular enough to identify an individual competitor's strategy.

European Union

Article 101 of the Treaty on the Functioning of the European Union (TFEU) prohibits agreements and concerted practices that restrict competition, subject to applicable exceptions.

The European Commission's 2023 Horizontal Guidelines discuss the exchange of commercially sensitive information, including the use of clean teams and independent trustees. They emphasize restricting access to information, limiting the exchange to what is necessary, and evaluating the competitive effects of the arrangement.

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India

Section 3 of the Competition Act, 2002, is central to agreements that cause or are likely to cause an appreciable adverse effect on competition in India. Section 3(3) is particularly relevant to horizontal arrangements among competitors, including arrangements involving price fixing, output restrictions, market allocation, or bid rigging.

A joint research project or safety-data pool is not automatically unlawful. Its purpose, design, effects, and the competitive relationship between participants matter.

For example, a joint pharmacovigilance clean room used to detect rare adverse events may have a legitimate scientific purpose. The same arrangement becomes more problematic if it also reveals planned price increases, individual discount strategies, or future production decisions.

5. Important case laws and their application

There is no single universally controlling judgment that establishes a complete legal test for pharmaceutical data clean rooms. The following cases provide relevant principles on privacy, identifiability, confidentiality, and information exchange between competitors.

Case 1. Justice K.S. Puttaswamy (Retd.) v. Union of India (2017)

Supreme Court of India · (2017) 10 SCC 1

Facts and legal issue: The litigation concerned the constitutional status of the right to privacy, including informational privacy, in the context of the Aadhaar framework.

Decision: The nine-judge Constitution Bench unanimously recognized privacy as a fundamental right protected by Article 21 and other freedoms under Part III of the Indian Constitution.

Application to pharmaceutical clean rooms:

Patient health information requires serious privacy protection.

Processing should have a lawful and legitimate purpose and appropriate safeguards.

Access restrictions and anonymization can support privacy protection but do not automatically establish constitutional or statutory compliance.

Large-scale data integration may create privacy risks even when individual datasets initially appear harmless.

Legal significance: This is a foundational Indian constitutional authority for protecting informational privacy. It is not a judgment specifically about data clean rooms, and its constitutional principles must be applied alongside the relevant statutory framework.

Case 2. Mr. X v. Hospital Z (1998)

Supreme Court of India · (1998) 8 SCC 296

Facts: The case concerned disclosure of a patient's HIV-positive status and the competing interests of medical confidentiality and protection of another person's health.

Decision: The Supreme Court held, in the circumstances of the case, that the patient's confidentiality did not override the relevant public-interest and third-party-protection considerations.

Application to pharmaceutical clean rooms:

Medical confidentiality is important but not absolute.

Disclosure must be assessed under the applicable legal duties and circumstances.

A clean room should not be treated as permission to use medical data beyond the legally authorized purpose.

Where safety information must be reported to a regulator, the technical architecture should allow lawful reporting without unnecessary disclosure to commercial collaborators.

Legal significance: The case illustrates the balancing of confidentiality against competing legal duties. It does not authorize unrestricted sharing of patient data with pharmaceutical companies.

Case 3. Patrick Breyer v. Bundesrepublik Deutschland (2016)

Court of Justice of the European Union · Case C-582/14, ECLI:EU:C:2016:779

Facts: The dispute concerned whether a dynamic IP address could constitute personal data when additional identifying information was held by another entity.

Decision: The Court held that information can qualify as personal data where the controller has legal means reasonably available to identify the individual using additional information.

Application to clean rooms:

Removing names and addresses does not necessarily anonymize a dataset.

A coded clinical record may remain personal data if a participant or another reasonably accessible party can reconnect it to a patient.

Re-identification risks must be evaluated across the participating organizations and available auxiliary datasets.

Legal significance: This case supports a contextual approach to identifiability. A clean-room operator cannot simply declare data anonymous because direct identifiers are absent.

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Case 4. T-Mobile Netherlands BV and Others v. Raad van bestuur van de Nederlandse Mededingingsautoriteit (2009)

Court of Justice of the European Union · Case C-8/08, ECLI:EU:C:2009:343

Facts: Competitors exchanged information concerning dealer remuneration during a meeting.

Decision: The Court held that information exchange capable of removing uncertainty about competitors' intended market conduct may constitute an anticompetitive concerted practice. A single meeting can be sufficient in the relevant circumstances.

Application to pharmaceutical clean rooms:

Suppose two competing manufacturers use a common analytics platform to evaluate market demand. If the system allows either company to infer the other's planned price changes or commercial strategy, the clean-room structure does not eliminate the competition-law risk.

Appropriate safeguards include:

Excluding future pricing and output data from the analysis.

Restricting raw-data access to independent personnel.

Prohibiting commercially sensitive outputs from reaching sales and pricing teams.

Documenting the legitimate purpose and necessity of each analytical query.

Legal significance: The case demonstrates that competition law examines the substance and effect of information exchange, not merely whether the companies communicate directly.

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Case 5. Dole Food Company, Inc. v. European Commission (2015)

Court of Justice of the European Union · Case C-286/13 P, ECLI:EU:C:2015:184

Facts: The case involved exchanges of commercially relevant information in the banana market.

Decision: The Court addressed how exchanges that reduce uncertainty concerning competitors' intended conduct can undermine independent competitive decision-making.

Application to pharmaceutical collaborations:

A clean room that produces highly granular product-level reports may reveal more than aggregate research findings. For instance, outputs about a rival's future product availability, planned supply changes, or commercial strategy may be sensitive even if the underlying dataset is never directly disclosed.

Legal significance: The case reinforces the importance of assessing the content and competitive implications of analytical outputs, rather than focusing exclusively on the security of the underlying data.

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Case 6. Federal Trade Commission v. Surescripts, LLC

U.S. District Court for the District of Columbia · No. 1:19-cv-01080

This U.S. antitrust litigation concerned alleged monopolization in electronic prescribing networks and related healthcare technology markets.

Relevance: It illustrates that healthcare data infrastructure and technology arrangements can raise competition-law issues when they affect market access, interoperability, or the ability of rivals to compete.

Application to pharmaceutical clean rooms: A dominant platform provider could create concerns if its clean-room services disadvantage competing research organizations, restrict access to essential datasets, or use privileged access to expand its own commercial position.

Important qualification: This case is not a direct ruling on pharmaceutical data clean rooms. Its relevance is by analogy to competition risks in healthcare data infrastructure.

Case 7. European Commission's 2023 Horizontal Guidelines

Commission Communication 2023/C 259/01

These are regulatory guidelines rather than a court judgment, but they are particularly relevant to designing clean-room arrangements between competitors.

The Commission explains that companies may use clean teams or independent trustees to limit access to commercially sensitive information. It also emphasizes restricting exchanges to what is necessary for the legitimate collaboration.

Application: An independent team should analyze the minimum data required and provide only approved results to business teams. The arrangement should not give competitors a practical means of coordinating prices, output, or other competitive conduct.

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Case-law synthesis

6. Pharmaceutical research, clinical trials and pharmacovigilance

Data clean rooms are particularly useful in three areas, but each has distinct legal requirements.

A. Clinical trial collaboration

A pharmaceutical company may combine clinical trial data from several hospitals to assess a medicine's efficacy or identify rare adverse events.

Legal requirements may include:

Valid authorization for the intended processing and disclosure.

Compliance with informed-consent commitments and applicable ethics approvals.

Protection of trial participants' identities.

Preservation of data integrity, traceability, and audit records.

Continued access for authorized regulators, auditors, and investigators.

Example: Two hospitals wish to combine patient outcome data to study a rare adverse reaction. A clean room may permit statistical analysis without giving either hospital unrestricted access to the other's patient records. The project must still have an appropriate legal basis and satisfy research-specific requirements.

B. Pharmacovigilance and drug safety

Pharmacovigilance involves the detection, assessment, understanding, and prevention of adverse effects associated with medicines.

Clean rooms may help identify safety signals across multiple healthcare systems. However, a platform must not interfere with mandatory adverse-event reporting, regulatory inspections, or lawful access to source records.

A contract that promises complete secrecy between collaborators cannot override a statutory reporting obligation.

C. Real-world evidence and precision medicine

Pharmaceutical researchers may analyze prescription records, laboratory results, genomic datasets, and treatment outcomes to assess how a drug performs in ordinary clinical practice.

Potential risks include:

Re-identification of patients with rare diseases.

Disclosure of sensitive genetic characteristics.

Unapproved secondary use of clinical records.

Biased findings arising from incomplete or unrepresentative datasets.

Commercial exploitation beyond the original research purpose.

For genomic data, particular care is needed because a patient's genetic information may also reveal information about biological relatives.

7. Intellectual property and trade-secret protection

A pharmaceutical clean room may contain more than personal information. It may also process confidential research and commercial data, such as unpublished clinical results, proprietary drug-response models, manufacturing parameters, or patent-related research.

The legal analysis should distinguish between the following:

Ownership: Who owns the underlying dataset, database rights, models, or other intellectual property?

Access rights: Which participants may access or query each dataset?

Permitted use: Can the output be used for research, regulatory submissions, publications, or commercial development?

Inventions: Who owns inventions or improvements developed through the collaboration?

Confidentiality: What information must remain secret, and for how long?

Exit rights: What happens to data, models, and derived outputs when the collaboration ends?

A clean-room agreement should expressly address ownership and permitted uses of derived datasets, statistical models, and research findings. The agreement should not assume that every output is automatically owned by the party that paid for the platform.

In India, contractual confidentiality, applicable intellectual-property legislation, and the law governing breach of confidence may be relevant. In other jurisdictions, trade-secret legislation may provide additional protection.

8. Essential contractual clauses

A well-designed pharmaceutical data clean-room agreement should cover the following areas.

Purpose and scope

Define the approved scientific or commercial objective and prohibit unrelated secondary uses.

Data roles and permissions

Identify each party's legal role, data rights, access permissions, and responsibilities.

Privacy and security

Specify access controls, encryption, pseudonymization, output screening, and incident procedures.

Competition safeguards

Exclude unnecessary competitively sensitive information and prevent access by commercial decision-makers.

Research and regulatory access

Preserve lawful access for regulators, ethics bodies, auditors, and safety reporting.

Intellectual property

Allocate rights in source data, analytical methods, derived outputs, and jointly developed inventions.

Retention and deletion

Establish retention periods, deletion requirements, and treatment of backups and derived datasets.

Audit and liability

Provide audit rights, cooperation obligations, breach notification, indemnities where appropriate, and dispute resolution.

The contract should also clarify whether the platform provider is merely a processor or service provider, or whether its actual activities make it an independent or joint controller under the applicable law. Labels in a contract are not necessarily decisive if the parties' real activities differ.

9. Regulatory enforcement and potential liability

An unlawful clean-room arrangement may create several types of liability at the same time.

The precise remedies depend on the applicable law, the parties involved, the nature of the violation, and the jurisdiction.

For example, a pharmaceutical company could have adequate cybersecurity but still violate competition law by using the platform to exchange future pricing information. Conversely, a collaboration could have a legitimate competition-law purpose but violate privacy law through unauthorized processing of identifiable health records.

10. Practical legality assessment

Use the following sequence before approving a pharmaceutical clean-room project.

Identify the purpose. Is the collaboration intended for clinical research, pharmacovigilance, health economics, commercial analytics, or another purpose?

Map the data. Identify personal data, health and genomic information, trade secrets, and commercially sensitive information.

Verify legal authority. Establish the lawful basis for processing, the permitted disclosures, relevant consent requirements, and any applicable research exception.

Assess identifiability. Test whether data can be re-identified by combining outputs with other information reasonably available to the participants.

Review competition risks. Determine whether participants compete and whether outputs could reveal pricing, production, bids, or future commercial plans.

Implement technical controls. Apply least-privilege access, query restrictions, output review, audit logs, and appropriate security testing.

Approve the contract and governance. Document roles, permissible uses, regulatory access, retention, liability, and independent oversight.

Monitor continuously. Reassess risks when datasets, participants, purposes, analytical capabilities, or applicable laws change.

11. Overall legal conclusion

Data clean rooms in pharmaceutical collaborations are generally a permissible technological and contractual arrangement, not a separate legal exemption.

Their legality depends on the underlying activity and the safeguards surrounding it.

A well-governed clean room can enable useful pharmaceutical research, pooled safety monitoring, and real-world evidence analysis while reducing unnecessary disclosure of patient and commercially confidential information.

Nevertheless, four principles are decisive:

Privacy: There must be a lawful basis for processing and appropriate protection of patient information.

Competition: The arrangement must not facilitate unlawful coordination between pharmaceutical competitors.

Research integrity: Clinical-trial, pharmacovigilance, ethics, and regulatory obligations must remain enforceable.

Accountability: Contracts, technical controls, access logs, and independent review must make the arrangement auditable.

The key judicial authorities are Puttaswamy, Mr. X v. Hospital Z, Breyer, T-Mobile Netherlands, and Dole Food, supplemented by applicable data-protection and competition guidelines. These authorities establish relevant legal principles, although none should be mischaracterized as a comprehensive judgment specifically approving pharmaceutical data clean rooms.

This is a general legal research explanation, not jurisdiction-specific legal advice. For a live collaboration, the final assessment should be based on the governing jurisdictions, the parties' competitive relationship, the data categories, and the precise intended uses.