In the world of finance, the “trusted third party” has always been a necessary go-between. If you needed to optimize a client’s portfolio across different banks, you went through a custodian. If you wanted to check for fraud signals against industry peers, you uploaded your data to a central utility. This reliance on intermediaries wasn’t perfect, but it was the only secure way to collaborate.
That’s no longer the case. A new class of technology is making it possible to work together without ever moving or revealing sensitive data. These are called privacy-enhancing technologies (PETs), and they allow institutions to collaborate without creating new risks. For the finance sector where privacy is not just a preference but a regulatory mandate and a competitive advantage this changes everything.
JPMorgan’s Prime Match platform is a great example. It allows hedge funds to participate in secure, private auctions. Orders are encrypted and matched, with only the overlapping trade volume being revealed. No one ever sees the full order book, which prevents information from leaking and others from getting front-run. The system works because no single party has to trust anyone else with its data.
The applications go far beyond trading. Consider these scenarios:
| Use Case | Challenge | How PETs Solve It |
| Portfolio Optimization | Asset managers want banks to model performance but won’t share their full portfolio, fearing it would reveal their strategy. | PETs allow the manager to encrypt the portfolio. The bank can run its models on the encrypted data and see only the results, not the underlying holdings. |
| Fraud Detection | Banks use shared services to flag suspicious accounts, but this requires centralizing sensitive customer data, which creates its own security risks. | Secure multiparty computation (SMPC) lets banks cross-check for bad actors without pooling their data. Each bank maintains full control of its information. |
The common thread here is replacing trust in an institution with trust in mathematics. Technologies like secure multiparty computation (SMPC), federated learning (FL), and trusted execution environments (TEEs) enable joint analysis without data exposure.
This shift is crucial because the old model of relying on intermediaries creates friction, adds cost, and slows innovation. PETs offer a better way forward: collaboration without surrender.
Of course, technology alone isn’t enough. Institutions must be able to govern who can run queries, on what data, and under which conditions. This is where platforms like Duality come in. We provide the governance and infrastructure to make PETs usable and performant in real-world financial environments. With our platform, you can:
This isn’t just theory. We’ve seen PETs deliver real results in finance, healthcare, and government all without compromising on security or privacy.
The era of the trusted third party is coming to an end. The future of finance will be governed, secure, and private by design. If you’re still depending on intermediaries to handle sensitive data, it’s time to ask yourself: what could you achieve if you didn’t have to?
