Introduction

    In today’s data-driven economy, companies often need to share information with partners or collaborators to unlock insights. But this must be balanced with strict data privacy regulations – from GDPR and CCPA for consumer data to HIPAA for health information – that impose safeguards on personal data. The challenge is how organizations can collaborate across corporate and industry boundaries without violating privacy rules or compromising sensitive data. In this post, we explore how companies share data in privacy-compliant ways through real examples in digital marketing, finance, and healthcare. We then give an overview of key privacy-preserving technologies (encryption, differential privacy, tokenization, federated learning) that enable such collaboration. Finally, we examine three Databricks solutions for secure data sharing – Marketplace, Delta Sharing, and Cleanrooms – and discuss when to use each.

Industry Examples: Privacy-Compliant Data Sharing

    Digital Marketing (Advertiser & Publisher): An online advertiser and a web publisher may want to combine their data to measure ad campaigns or target audiences more effectively. Privacy laws and user consent requirements mean they cannot simply swap user-level data. Instead, they might use a data clean room, a secure environment where both parties can analyze combined data without exposing individuals’ identities. For example, the advertiser could learn how many of its customers saw an ad on the publisher’s site and later made a purchase but only see aggregated statistics – not the publisher’s entire user list. By hashing or anonymizing customer identifiers and enforcing privacy rules in the clean room, the advertiser and publisher gain insights while staying compliant with GDPR/CCPA.

    Financial Services (Banking): Banks benefit from sharing data to detect fraud or assess credit risk but must protect customer confidentiality. A group of banks might want to build a joint fraud detection model; directly pooling their transaction data would violate privacy policies and laws. Instead, they can use federated learning or similar privacy-enhancing techniques. In a federated setup, each bank trains a model on its own data and shares only the learned model parameters (not raw customer data) to create a combined model. This way, they collaborate on improving fraud detection without exposing any individual’s data. Such approaches let banks leverage collective data insights while still adhering to GDPR and other regulations.

    Healthcare (Providers & Research): Hospitals and healthcare providers often share patient data with researchers to advance medical knowledge, but under HIPAA they must ensure patient privacy. The common solution is de-identification: removing or obscuring personal identifiers so the data can no longer be linked to a specific patient. For example, multiple clinics could contribute de-identified patient records to a research database to study disease surveillance or treatment outcomes. Because the data is stripped of names, contact info, and other direct identifiers, it can be shared for research without breaching HIPAA rules. This enables valuable medical insights to be derived from large, diverse datasets while maintaining patient confidentiality.

Privacy-Preserving Data Sharing Technologies

    To enable such scenarios, organizations rely on technologies that protect privacy while still allowing data to be useful. Four key mechanisms include:

• Encryption: Converting data into cipher text to prevent unauthorized access. Companies encrypt data in transit and at rest as a baseline security measure. This way, even if data is intercepted, it remains unreadable. Advanced techniques like homomorphic encryption allow computations on encrypted data (useful when one party wants to analyze another’s data without seeing it).
• Tokenization: Replacing sensitive data with non-sensitive placeholders called tokens. For example, a person’s Social Security number or customer ID can be substituted with a random token string. Tokens let companies join or share datasets on a common key without exposing the actual personal information. This minimizes the spread of personally identifiable information (PII) and helps comply with privacy laws by keeping raw identifiers private.
• Differential Privacy: A technique for sharing aggregate information while mathematically guaranteeing that individual entries remain confidential. It works by adding carefully calibrated noise to query results or data. For instance, a company could share user behavior statistics with a partner, but with differential privacy applied the partner cannot tell if any specific user is in the dataset. This allows analysis of trends and patterns (e.g., average spend or visit frequency) without revealing anything about any one person.
• Federated Learning: A distributed machine learning approach where multiple parties train a shared model without exchanging their raw data. Each organization (or device) trains the model on its local data and shares only the model updates, which are then aggregated to improve the global model. For example, two banks can build a better credit scoring model together without ever exchanging customer databases. Federated learning often combines with encryption or differential privacy on the model parameters for extra protection, enabling collaborative AI across silos in a compliant way.

These privacy-preserving technologies, often called Privacy Enhancing Technologies (PETs), can be combined with strong governance (access controls, auditing, consent management) to allow data sharing that meets the requirements of GDPR, CCPA, HIPAA, and other laws. Now, let’s see how Databricks leverages these concepts in its secure data sharing solutions.

Databricks Solutions for Secure Data Collaboration

    Databricks provides integrated tools to help organizations share data safely on its platform. We’ll look at three offerings and when to use each.

Databricks Marketplace: Monetizing and Exchanging Data

    Databricks Marketplace is an open marketplace where organizations can buy and sell datasets and AI assets. It provides a secure platform for data providers to monetize their data and for consumers to easily find and integrate third-party data. Providers list data products (like datasets or ML models) along with usage terms, and consumers can subscribe through the platform. The marketplace handles secure data delivery and governance so that data is shared in a controlled way. This is ideal for sharing non-sensitive or aggregated data broadly. For example, a financial data vendor can offer historical market data to many customers via the marketplace. The benefit is speed and scale in data sharing without custom integration.

Databricks Marketplace’s Private Exchange capability enables organizations to securely share data products—such as datasets, AI models, and analytics tools—with a select group of consumers. Unlike the public marketplace, where data products are accessible to all Databricks users, private exchanges restrict visibility and access to invited members only.

Delta Sharing via Unity Catalog: Live Cross-Organization Sharing

    Delta Sharing is Databricks’ solution for direct, secure exchange of data between organizations. It’s an open protocol (now part of the Linux Foundation) that lets you share data across different platforms and clouds in real time. Within Databricks, Delta Sharing is managed through Unity Catalog for fine-grained security and auditing. A data provider can grant access to specific tables or files, and a recipient can query that data live – without needing to copy the data or be on the same platform. This ensures the provider retains control and avoids data duplication. Delta Sharing is best when you have a trusted partner or customer who needs up-to-date data. It delivers data efficiently and openly, but since the recipient could save their own copy, the shared data should be something you’re allowed to share (often under a data agreement or after anonymization).

Databricks Cleanrooms: Collaborative Analytics with Privacy

    Databricks Cleanrooms enable two or more parties to analyze data together in a protected environment so that no raw data is exchanged. It’s like a neutral “safe room” where each company brings in its data and joint analysis is done with strict controls. A cleanroom allows, for example, an advertiser and a publisher to match and analyze campaign data without either side seeing the other’s individual customer records. They get aggregated insights (e.g. how many customers saw an ad and then purchased) with privacy intact. The big advantage of cleanrooms is unlocking data partnerships that were previously off-limits due to privacy concerns – essential in industries like healthcare, finance, and marketing. The trade-off is added complexity and compute cost, since analysis has to happen in this controlled environment. Cleanrooms are the go-to when data is highly sensitive or regulated, and when trust needs to be enforced by technology rather than just by contracts.

Conclusion

    Modern businesses don’t have to choose between data collaboration and privacy compliance – they can have both. By using technologies like encryption, tokenization, differential privacy, and federated learning, and by leveraging platforms such as Databricks that incorporate these safeguards, companies can share data to drive insights while respecting privacy laws and customer trust. Databricks Marketplace, Delta Sharing, and Cleanrooms address different sharing needs (from broad data distribution to one-to-one sharing to zero-exposure collaboration), showing that there’s a secure solution for every scenario. With the right approach, organizations can unlock the value of shared data and innovate with partners across industries, turning privacy requirements into a strength rather than an obstacle.