In a global economy driven by data, the ability to share information across borders is essential for innovation, collaboration, and business growth. However, as enterprises navigate an increasingly complex web of data protection regulations, such as GDPR, CCPA, China’s PIPL, and others, cross-border data sharing has become both a necessity and a liability.
Enter Privacy-Enhancing Technologies (PETs), a class of tools designed to preserve privacy while still enabling the safe analysis, transfer, and usage of data across jurisdictions.
What Are PETs?
Privacy-Enhancing Technologies allow organisations to use, share, and analyse sensitive data without exposing the raw data. These technologies include:
- Homomorphic encryption enables computations on encrypted data without needing to decrypt it
- Secure multiparty computation (SMPC) allows multiple parties to compute a function jointly without sharing individual data
- Federated learning – trains models across decentralised data sources without moving data
- Differential privacy – adds statistical noise to datasets to prevent the identification of individuals
- Zero-knowledge proofs – prove the truth of a statement without revealing the underlying data
Why PETs Matter in Cross-Border Contexts
Cross-border data flows are critical for multinational organisations, but they come with risk. Regulatory fragmentation means that what’s legal in one country might be a violation in another.
PETs allow enterprises to:
- Maintain regulatory compliance when sharing data across borders
- Enable secure analytics and collaboration with third-party vendors or partners
- Preserve customer trust by protecting sensitive information
- Innovate responsibly without compromising privacy
For example, a global health organisation using federated learning can train an AI diagnostic model on hospital data from multiple countries, without transferring patient data across borders.
Enterprise Use Cases
- Financial Services: Banks use SMPC to jointly analyse fraud patterns without exposing client details
- Healthcare: Hospitals train ML models across distributed data sources with federated learning
- Retail: Global retailers anonymise customer data for cross-border analytics using differential privacy
- Public Sector: Governments share insights on disease outbreaks or migration patterns using zero-knowledge proofs
Challenges to Adoption
Despite the promise, PETs aren’t plug-and-play. They require:
- Specialised expertise in cryptography and secure systems
- Performance trade-offs, especially with computation-heavy methods
- Cost considerations for implementation at scale
- Regulatory recognition to ensure legal defensibility in audits
Still, the business case is growing stronger, and regulators are taking note. In 2023, the UK’s Information Commissioner’s Office (ICO) and Singapore’s PDPC jointly released a guide to PETs in cross-border contexts, marking a shift toward wider acceptance.
What’s Next?
As AI and data-centric applications expand, PETs will play a crucial role in enabling trustworthy innovation. Expect to see:
- Increased standardisation and open-source tools
- More guidance from regulators
- Integration with cloud platforms and enterprise analytics tools
- Growth in PET-as-a-service offerings
Bottom Line: Privacy-enhancing technologies aren’t just a defensive measure, they’re a strategic enabler. For enterprises operating globally, PETs offer a path to unlock value from data while respecting privacy and regulatory boundaries.
The future of cross-border data sharing is private, secure, and powered by PETs.
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