Unlocking Data Value through Privacy-Preserving Collaboration

Introduction:
Data has been likened to the new oil, powering innovation and economic growth. Yet a vast amount of the world’s data remains “privatized” – locked away in silos due to privacy concerns, proprietary interests, or regulatory restrictions. De-privatising data is about finding ways to safely share and utilize this untapped data for collective benefit. Between 2023 and 2025, we have seen a surge of interest in frameworks and technologies that enable organizations to break down data barriers without compromising privacy or competitive advantage. The European Union even introduced a new law, the Data Governance Act, explicitly to “enhance trust in voluntary data sharing for the benefit of businesses and citizens.”digital-strategy.ec.europa.eu The motivation is clear: the societal and economic potential of data is enormous – from better healthcare and personalized treatments to smarter city planning and AI-driven services – but much of it isn’t realized because data sharing remains limited by low trust and technical hurdlesdigital-strategy.ec.europa.eu. In this article, we explore how recent developments in technology (like federated learning, synthetic data, and encryption) and policy (data collaboratives, data altruism frameworks) are converging to de-privatize data. We’ll discuss examples of tools and case studies, and examine the opportunities unlocked, as well as challenges that must be overcome.

New Technologies for Privacy-Preserving Data Sharing

Modern privacy-preserving techniques are at the heart of de-privatizing data. They allow multiple parties to collaborate with data without directly exposing sensitive information. Here are some key developments from 2023–2025:

• Federated Learning: Federated learning (FL) has matured from a research concept to real-world deployments. FL enables training machine learning models across datasets that reside in different locations (e.g., different companies or hospitals) without raw data ever leaving its source. Instead, models are trained locally on each dataset, and only the learned parameters or gradients (sometimes encrypted or noise-protected) are shared and aggregated to produce a global model. This approach gained traction in healthcare and finance recently. For example, in 2023 a consortium of hospitals used federated learning to jointly train an AI model for tumor detection, achieving performance close to a single centralized dataset, all while each hospital kept patient data on their own servers. This collaboration was previously impossible due to privacy laws, but FL provided a workaround by ensuring no patient records were exchanged – only model updates. Studies and case reports over the last two years indicate federated models can match the accuracy of centrally trained models in certain tasks, proving that “share the model, not the data” is a viable strategy. Tools like Flower and Federated TensorFlow have made FL implementation more accessible, and we’ve seen tech companies (Google, NVIDIA) release FL frameworks specifically for cross-industry use (e.g., NVIDIA’s FLARE for healthcare).

• Synthetic Data & Anonymization: As discussed in the previous article, synthetic data generation is a powerful method to deprivatize data. By generating an artificial dataset that preserves statistical patterns of the real sensitive data, organizations can share or analyze data without exposing real personal records. In 2024, Microsoft Research highlighted private synthetic data as a key to bridging innovation and privacy, noting that synthetic datasets let teams train AI models or conduct analytics “without compromising individual privacy,” enabling compliance with GDPR and similar lawsmicrosoft.com. For instance, banks have started using synthetic customer data for developing new fraud detection algorithms; since the synthetic data contains no actual customer identities, it can be freely shared with fintech partners or used in external data science competitions. However, it’s important to ensure the synthetic data is properly anonymized – naive approaches can inadvertently leak information. To tackle this, researchers are combining synthetic data with techniques like differential privacy, which adds statistical noise in a principled way. Recent papers (2023–2024) introduced methods to train generative models with differential privacy guarantees, so that the synthetic data has provable limits on how much it reveals about any one individualmicrosoft.commicrosoft.com. One approach injected noise during LLM fine-tuning on private text data, allowing the model to generate useful text while guaranteeing no original sensitive sentence can be reconstructedmicrosoft.com. Another approach used pre-trained model APIs in a privacy-preserving manner to synthesize data without any custom trainingmicrosoft.com. These advancements mean organizations can increasingly rely on “shareable” synthetic versions of their datasets for collaboration, overcoming the traditional privacy-utility tradeoff.

• Secure Multi-Party Computation (SMPC) and Homomorphic Encryption: For scenarios where actual data values need to be jointly analyzed (rather than training a black-box model), cryptographic techniques have seen progress. Homomorphic encryption allows computations on encrypted data – meaning two parties can combine their datasets, run queries or machine learning algorithms on the combined set, and get results, all without ever decrypting each other’s data. While homomorphic encryption used to be notoriously slow, by 2025 more efficient schemes and specialized hardware have improved its practicality (for example, there are reports of simple machine learning tasks like linear regression being done homomorphically across bank datasets). Similarly, SMPC protocols enable a handful of parties to compute a function (like an aggregate statistic or AI inference) such that each party learns the final result but learns nothing else about the others’ data. These techniques remain complex to implement, but they are being productized in the form of data clean rooms and confidential computing services. A data clean room is a secure environment (often provided by a cloud vendor or neutral third party) where multiple datasets can be analyzed together under strict controls – participants only see aggregated results and cannot remove raw data. Big cloud providers (Google, AWS, Azure) and specialists like InfoSum have launched clean room offerings, particularly for advertising and healthcare analytics. Such technologies are “revolutionizing secure collaboration, allowing computations on encrypted data without decryption” and enabling cross-organization data projects in sensitive sectors like healthcare and finance that were previously impossiblenumberanalytics.com. For example, pharmaceutical companies have used secure data sharing platforms to jointly analyze patient data from clinical trials run by different companies, to detect safety signals that wouldn’t be apparent in any single trial. None of the companies actually see each other’s raw data, but the insights emerge from the pooled analysis.

Frameworks and Initiatives for Data Collaboration

Technology alone isn’t enough; governance and frameworks play a crucial role in de-privatizing data. Recent policy moves and collaborative initiatives address the trust and incentive issues that often block data sharing:

• Data Governance Act (EU) and Data Altruism: In late 2023, the EU’s Data Governance Act (DGA) came into effect, aiming to jump-start data sharing across Europe. The DGA establishes trusted mechanisms like data intermediation services – essentially certified third-party platforms or brokers that facilitate data sharing in a neutral, secure waydigital-strategy.ec.europa.eudigital-strategy.ec.europa.eu. It also introduces the concept of “data altruism,” where individuals and companies can voluntarily donate data for the public good under a common framework. Organizations can register as Data Altruism Organizations to collect and curate such data, with legal protections in place. The idea is to create a culture and infrastructure for sharing data for beneficial projects (scientific research, policy making, etc.) with transparency and trust. While still early, these moves reflect a broader trend: governments are recognizing that the old approach of each entity hoarding its data is suboptimal, and they are trying to enable data collaboratives that preserve rights. For businesses, this also means new opportunities – under the DGA, a company might share industrial data with a pooling service to help create, say, a sector-wide AI model for predictive maintenance, with assurances that no competitor will misuse their contributed data. In return, they benefit from insights that come out of the combined dataset. The DGA explicitly notes that “a wealth of knowledge can be extracted from protected data without compromising its confidential nature” and calls for technical solutions like anonymization and secure environments to facilitate thisdigital-strategy.ec.europa.eudigital-strategy.ec.europa.eu.

• Industry Data Commons and Exchanges: Outside of government, various industries have launched their own data sharing consortiums. For example, the Mobility Data Space in Europe (launched 2023) allows automotive and transport organizations to share traffic and vehicle data under agreed rules. In finance, several banks and fintechs formed a data-sharing alliance to jointly fight fraud and financial crime, using privacy-preserving analytics on shared transaction data. We also see more data marketplaces where companies can monetize their anonymized datasets. These marketplaces often incorporate privacy tech by design – buyers get insights or trained models rather than raw personal data. According to one analysis, such data collaboration and sharing practices correlate with business success: companies that leverage collaborative data practices are 1.5 times more likely to exceed industry revenue averagesnumberanalytics.comnumberanalytics.com, as they unlock new value streams and efficiencies.

• Open Data and Public Data Commons: Government open data initiatives continue to grow, and in the period 2023–2025 there’s been focus on making more high-value datasets available (with privacy safeguards). For instance, many cities have released anonymized mobility data to help tech firms and researchers improve urban planning and transportation services. There’s also a push for creating data commons for research – shared pools of data that multiple stakeholders (universities, companies, non-profits) contribute to and govern collectively. One example is the proposed International COVID Data Alliance turning into a broader health data commons for pandemic and biosecurity data, with frameworks to allow patient data from different countries to be used in emergency research safely. The concept of a “public data commons” is gaining traction as a way to treat certain datasets (like weather data, satellite imagery, genomic data) as critical infrastructure that should be widely accessible to spur innovation, rather than owned by one entitymedium.com. We expect to see more developments here as AI’s hunger for data intersects with public interest.

Opportunities Unlocked

De-privatizing data has significant upsides for both organizations and society at large:

• Better AI Models and Insights: The most immediate benefit is the ability to build more accurate and unbiased models by training on richer, more diverse data. Many AI failures or biases in the past stem from narrow training data. By combining data from multiple sources (while respecting privacy), data scientists can eliminate blind spots. For example, a medical AI that learns from hospitals across different regions and demographics will be more robust and fair than one trained on a single hospital’s data. In the enterprise, data collaboration can break down internal silos too – merging data from different departments or subsidiaries to get a 360° view of operations. One survey found that 75% of organizations implementing comprehensive data collaboration reported improvements in product quality and faster time-to-market for software projectsnumberanalytics.com. Essentially, more eyes and more data on a problem yield better solutions.

• New Business and Research Frontiers: When data moves more freely (in a controlled way), it enables projects that were previously stalled. Consider drug discovery: pharma companies traditionally guard their clinical data, but by pooling anonymized trial results, they can use AI to find patterns across a much larger patient population, potentially identifying drug repurposing opportunities or early safety signals. In climate and energy, sharing data (e.g., across power grid operators or between governments and utilities) can help optimize resource usage and accelerate green innovation. Companies can also create new revenue streams by sharing data: for instance, an automotive firm might provide privacy-preserving vehicle telemetry data to smart city planners or insurance companies for a fee, benefiting all parties. These kinds of data partnerships are part of digital transformation strategies. A McKinsey study in 2023 noted that embracing data sharing and collaboration is a hallmark of digitally mature organizations and directly ties to higher success rates in AI initiativesnumberanalytics.com.

• Societal Benefits: On the societal side, de-privatizing data can drive public good. We saw during the COVID-19 pandemic how critical data sharing was to track the virus and develop vaccines. Going forward, similar collaborative data efforts can tackle challenges like disease outbreaks, disaster response, or global supply chain issues. For example, sharing supply chain and shipping data (with proper confidentiality) can help anticipate shortages or bottlenecks and coordinate responses. In agriculture, farmers and companies sharing data about crop yields and weather can lead to improved food security models. Essentially, many societal problems don’t respect organizational boundaries, and data needs to flow to address them effectively. Privacy-preserving techniques ensure this can be done ethically and in compliance with laws, maintaining public trust.

• Leveling the Playing Field: De-privatizing data can also reduce data monopolies. Currently, a handful of tech giants hold disproportionate amounts of data, giving them huge advantages in AI. If mechanisms exist for smaller players or startups to access large-scale datasets (be it through data trusts, public datasets, or cooperative sharing agreements), it democratizes innovation. For instance, the DGA’s provision to limit exclusive rights to public sector datadigital-strategy.ec.europa.eu means no single company can lock up valuable government data – it should be available to all innovators under fair terms. This encourages competition and creativity, as more minds can work on data-driven solutions.

Challenges and Safeguards

While the opportunities are exciting, there are notable challenges in de-privatizing data:

• Privacy and Re-identification Risks: The foremost concern is that even anonymized or synthetic data could be misused to identify individuals. Techniques exist to merge datasets and potentially re-identify people (“jigsaw attacks”), especially if shared data is high-dimensional. Thus, rigorous privacy risk assessment is essential. Differential privacy, as mentioned, provides mathematical guarantees but often at the cost of some data utility. There is ongoing research into better privacy metrics and evaluation techniquessciencedirect.comarxiv.org to help decide if a dataset is safe to release. Organizations need to stay updated on best practices and perhaps employ privacy experts or auditors when engaging in data sharing. Regulators too are updating guidelines – e.g., in 2024 some data protection authorities released guidance on assessing re-identification risk in synthetic data. Ultimately, a combination of technical and legal safeguards (contracts prohibiting re-identification, severe penalties for misuse) is needed to enforce privacy in collaborative data environments.

• Trust and Cultural Barriers: Building trust between organizations to share data is non-trivial. Companies fear losing control over a valuable asset or leaking competitive info. As noted, many fear data sharing could mean a loss of competitive advantage or risk of data misusedigital-strategy.ec.europa.eu. It often takes a neutral party or a strong incentive (like a common threat or common goal) to bring parties to the table. Data intermediation services and clear legal frameworks (like the DGA) aim to provide that neutral ground. But it will also require a cultural shift: viewing data collaboration not as a liability but as a strategic benefit. Internally, some data governance teams used to traditional siloed control might resist new sharing initiativesnumberanalytics.com. Change management and demonstrating small wins (via pilot projects) can help overcome this.

• Technical Complexity and Cost: Implementing privacy-preserving technologies can be complex and computationally heavy. Homomorphic encryption and SMPC can significantly slow down computations and require expertise to deploy correctly. Federated learning can be tricky in terms of communication costs and ensuring all parties’ updates are properly aggregated (plus, if one party’s data is low quality, it could affect the global model). These overheads mean that in some cases, initial attempts at data collaboration might be slower or more expensive than traditional silo analysis. However, as the tech improves and economies of scale kick in (e.g., cloud services offering these capabilities out-of-the-box), the cost should come down. Still, managers must weigh the costs and ensure they have the right talent or vendor support to implement these solutions securely.

• Regulatory Uncertainty: The legal landscape around data sharing is still evolving. While GDPR, for example, doesn’t forbid sharing data if done with consent or proper basis, there is uncertainty on things like: Is synthetic data still personal data? How to handle cross-border data sharing when laws differ? New laws like the EU AI Act (expected around 2024) also have provisions that might require transparency and risk management for models trained on shared data. Companies venturing into data collaboration should seek legal guidance to navigate these issues, and perhaps participate in standards development. On the flip side, regulators are equally challenged to keep up with technical advances. Clearer standards (for anonymization, for secure data exchange, etc.) will help, and likely we’ll see more certification schemes (for instance, a certification for “privacy-preserving data platform”) to signal trust.

• Quality and Interoperability: Making data shareable isn’t just about privacy – it’s also about making it useful to others. Data might need to be cleaned, standardized, or formatted in common schemas to be merged. Different organizations have different definitions and quality issues. Initiatives like data commons often spend a lot of effort on agreeing to ontologies and data standards. Poor data quality or mismatch can derail a collaboration. Therefore, part of de-privatizing data is also investing in data standardization and metadata so that whoever accesses the data can understand and use it correctly. This is more of a mundane challenge but a critical one: without alignment on schema and context, combining data can lead to garbage-in, garbage-out.

Toward a Balanced Approach

For data scientists and operational managers looking to harness these developments, a balanced approach is key. Start with clear use cases where the value of shared data is evident and outweighs the effort – for example, a joint fraud detection model across banks, or a cross-company supply chain forecast. Engage stakeholders early, including legal, compliance, and the people whose data might be shared (to address concerns and build trust). Employ a “privacy by design” mindset: bake in privacy measures from the get-go rather than as an afterthought. This might mean using off-the-shelf privacy libraries, or ensuring any shared dataset is aggregated or sampled to reduce identifiability.

Another recommendation is to use intermediaries or platforms that specialize in secure data collaboration. If you’re not a crypto expert, leveraging a reputable data clean room service or an established federated learning platform can accelerate adoption while minimizing risk. These services often come with certifications or at least proven track records. It’s also wise to put formal agreements in place – data sharing agreements that specify allowable use, security requirements, and liabilities in case of breaches or misuse. Such agreements, combined with technical controls, create layers of protection.

Finally, measuring and communicating the benefits gained is important to sustain the effort. Keep track of metrics like how much faster a model was developed due to data sharing, or how much broader a customer insight was achieved. These success stories will help in scaling up data collaboration efforts. They will also help policymakers see the positive impact, which can lead to more supportive regulations or funding for data infrastructure.

Conclusion:
De-privatizing data represents a paradigm shift in how we think about information ownership and collaboration. The period of 2023–2025 has shown that with the right tools and frameworks, it’s possible to unlock the immense value in previously siloed data while still respecting privacy and proprietary interests. Techniques like federated learning, synthetic data generation, and secure computation are not just theoretical—they are already enabling new partnerships and innovations on the ground. Policies are catching up, with laws like the DGA actively encouraging trustworthy data sharing ecosystems.

For data scientists and managers, this is an exciting frontier: it means access to richer datasets, opportunities for cross-organization AI projects, and the ability to solve problems that no single dataset could solve alone. It also requires a thoughtful approach to navigate the technical and ethical complexities. Those who learn to leverage privacy-preserving collaboration effectively will likely lead in building the next generation of data-driven solutions. In essence, de-privatizing data is about transforming data from a closely guarded asset into a shared resource – done carefully, it promises a future where the whole is greater than the sum of its parts, driving both business value and societal progress.