Synthetic Data: The Key to Unlocking Privacy and Sustainability in the Digital Age

Ivana Bartoletti, the Global Privacy Officer at Wipro, explains why synthetic data might be the key to unlocking privacy and sustainability in today’s digital age. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Data fuels the economy like oil once powered the Industrial Revolution. However, unlike oil, data can be reused endlessly. Yet, the current “data extractivism” model—where vast amounts of data are harvested, stored, and exploited—raises serious privacy, ethical, and environmental concerns. Reflecting its growing relevance, the global synthetic data generation market was valued at $310 million in 2023 and is projected to grow at a 30.4 percent CAGR through 2029. This promising innovation imitates real data without replicating sensitive details, paving the way for a more privacy-conscious and sustainable approach to data use.

The Data Economy: Scale and Consequences

Approximately 2.5 quintillion bytes of data are created daily—from social media interactions to IoT sensor readings. This deluge of information is expected to grow exponentially, with projections estimating 463 exabytes of daily data generation by 2025–the equivalent of 212,765,957 DVDs of data. While this data underpins artificial intelligence (AI) development and decision-making, its unchecked collection has far-reaching consequences.

Privacy at Risk

Personal data collection is integral to today’s economy, but it often comes at the expense of individual privacy. Data breaches and the misuse of aggregated information have led to widespread trust issues. The ability to create detailed profiles of individuals poses risks, enabling discriminatory practices in areas like insurance, credit, and employment.

Moreover, transparency remains a persistent challenge. Most users need further insight into how their data is collected, stored, and monetized. This opacity undermines trust and tilts the balance of power toward tech companies.

Environmental Impact

The data economy’s ecological footprint is expanding, with data centers—responsible for storing and processing vast amounts of information—currently accounting for about 1 percent to 1.3 percent of global electricity consumption. This figure is projected to rise significantly; the International Energy Agency forecasts that electricity consumption from data centers, AI, and the cryptocurrency sector could double by 2026, potentially reaching over 1,000 terawatt-hours. This surge is comparable to Japan’s annual electricity consumption. The energy-intensive nature of these facilities contributes substantially to carbon emissions, raising serious environmental concerns.

Enter Synthetic Data

Synthetic data, artificially generated to replicate the statistical patterns of real data, presents a way to navigate these challenges. Simulating real-world datasets without compromising individual privacy offers a blend of innovation and responsibility:

• Privacy protection: Synthetic data eliminates the use of real personal information, reducing exposure to breaches and unauthorized access. Its alignment with Privacy-Enhancing Technologies (PETs) makes it a valuable tool for compliance with regulations like the GDPR.
• Reduced data collection: Synthetic data lessens the need for extensive personal data collection by creating high-quality datasets. This shift addresses privacy concerns associated with “data extractivism.”
• Environmental sustainability: Using synthetic data can minimize the storage and processing requirements for vast amounts of real data, lowering the energy consumption of data centers.
• Addressing bias: Synthetic data allows for the generation of datasets that include underrepresented groups or rare scenarios, addressing biases in existing data. For example, it can help develop AI models that better serve diverse populations in healthcare.
• Cost efficiency: Collecting real-world data can be expensive and time-consuming. Synthetic data reduces these burdens, especially for rare or difficult-to-capture scenarios.

Challenges and Risks

While synthetic data holds promise, it is not without its pitfalls.

• Re-identification risks: Synthetic datasets, if too closely aligned with real-world data, may inadvertently allow for the identification of individuals. Robust safeguards are necessary to mitigate this risk.
• Data quality concerns: The utility of synthetic data depends on its ability to reflect the complexities of real-world data. Poorly generated datasets can lead to flawed insights and unreliable AI models.
• Computational demands: Generating high-quality synthetic data requires advanced algorithms and significant computing resources, which can offset some environmental benefits.

A Balanced Approach

Synthetic data holds significant promise but must be pursued responsibly. Developing robust methodologies is critical to ensuring that synthetic datasets reflect real-world complexities while safeguarding privacy. This involves leveraging advanced techniques to balance data utility with protections against re-identification risks.

Ethical and regulatory considerations are equally vital. Clear guidelines for creating and using synthetic data will promote fairness, prevent misuse, and ensure inclusivity. Regulatory oversight can help align synthetic data practices with global data protection laws, fostering trust and accountability. Innovation in data generation tools is also essential, with a focus on balancing computational efficiency and the production of high-quality, representative datasets.

Synthetic data offers a transformative solution to challenges in data privacy and environmental sustainability. By adopting a balanced approach—combining ethical safeguards, technical innovation, and regulatory alignment—organizations can unlock their full potential. As the synthetic data market continues its rapid growth, thoughtful integration into operations can shape a future that respects individual privacy and the planet.