The introduction of the General Data Protection Regulation (GDPR) in Europe in 2018 sent shockwaves through the global data ecosystem. Impacting more than 28 million businesses worldwide, it marked a watershed moment in how organizations handle personally identifiable information (PII). Data protection regulations, often referred to as privacy laws, have since become a defining trend, as real-world data becomes increasingly regulated. These laws mandate businesses to protect customer data and ensure its use complies with strict privacy standards.
Before 2018, privacy was largely legal “Check-the-box” Exercise. Today it has evolved into a technical challenge. With more than 130 countries now implementing privacy laws, businesses must ask themselves: How can they meet these legal and technological requirements without stifling innovation?
The role of privacy technologies
Privacy laws impose legal and technical obligations on businesses. While legal teams can navigate compliance aspects, technical challenges require robust privacy engineering solutions. Privacy technologies address these challenges by determining where and how to add them “noise” in the data. The three main methods are:
1. Data Anonymization: This method includes information sanitization through techniques such as suppression, pseudonymization, normalization, swapping, or disturbance to remove personal identifiers. In other words, this method destroys information to protect privacy.
Advantages:
- Ensures privacy through de-identification.
- Easy to implement for structured datasets.
Difficulties:
- Vulnerable to re-identification risks (eg, gender, zip code, and date of birth can identify 87% of people worldwide).
- Data based on the extent of anonymization greatly reduces usability.
2. Data Encryption: This method secures data by converting it into ciphertext using mechanisms such as Public Key Infrastructure (PKI), Homomorphic Encryption (HE), Secure Multi-party Computation (SMPC), Zero-Knowledge Proofs (ZKP), and Private Set Intersection (PSI). .
Advantages:
- Strong protection against unauthorized access.
- Complies with most data protection laws.
Difficulties:
Key management complexity, risk of key collisions, and potential data breaches.
Data availability is significantly reduced, limiting its usefulness for analysis.
3. Synthetic Data: Synthetic data: This method generates synthetic data using generative models such as generative adversarial networks (GANs), diffusion models, and language models (LMs), with no direct link to real-world data.
Advantages:
- Provides high data utilization while maintaining strong privacy.
- Enables rapid data sharing for model training and software testing.
Difficulties:
- Potential risks of overfitting or creating outliers.
- Advanced data expertise and computing resources are required.
Three pillars of AI development
To understand the role of synthetic data, we must consider the three basic pillars of AI development: computation, algorithms, and data.
- Calculate: The infrastructure that powers AI, led by NVIDIA (GPUs), Google (TPUs), and AWS (cloud computing).
- Algorithm: Models developed by pioneers such as OpenAI (Transformer), DeepMind (Reinforcement Learning), and Meta (Large Scale Language Model).
- Data: The life blood of AI systems, is becoming an increasingly scarce and highly regulated resource.
Data constraints
Real data is limited, valuable, and increasingly difficult to use due to three main challenges:
- Privacy Risk: Customer data contains sensitive information.
- Intellectual Property (IP): Publicly available data cannot always be commercialized.
- Data scarcity: High-quality data for AI/ML is limited, with studies projecting its exhaustion for training purposes between 2026 and 2032.
Stanford University’s 2024 AI Index report underscores this challenge, predicting that high-quality language data may run out by 2024, low-quality language data in two decades, and image data by the late 2030s. Demand for training datasets is expected to grow exponentially, with Epoch AI estimating that 80,000x more data will be needed by 2030 to effectively scale AI models.
Synthetic Data: A Transformational Solution
Synthetic data is emerging as the key to overcoming data scarcity, privacy, and IP barriers. By creating artificial datasets that mimic real-world characteristics, synthetic data allows businesses to:
- Ensure privacy: Anonymizes data while maintaining compliance with global regulations.
- Protect IP: Produces proprietary datasets without relying on publicly available data.
- Overcoming scarcity: Simulating diverse and high-quality datasets for AI/ML applications.
Unlike real-world data, synthetic data is scalable, diverse and customizable, making it a game-changer for AI/ML development.
The future of AI will be built on a foundation of privacy
As the UK Financial Conduct Authority notes, synthetic data is not bound by data protection obligations such as GDPR unless it is linked to identifiable individuals. It positions synthetic data as the cornerstone of a privacy-preserving, innovation-driven AI ecosystem.
In an era where data is the new IP, businesses must embrace synthetic data to responsibly fuel AI advancements. By addressing privacy risks, regulatory compliance, and data scarcity, synthetic data demonstrates that better data equals better AI, paving the way for more sustainable and ethical innovation in AI development.
See the context below for when and where synthetic data can be applied.