Powering larger insights with privacy-preserving computation
January 30, 2020
January 30, 2020
Companies today know they have to share data to succeed. Enterprise partnerships create new advantages and opportunities for growth, and the combined data from across the ecosystem offers insights and value that’s impossible to uncover alone.
But even as companies are eager to unlock the power of pooled data, they’d like to maintain control over their own. In some cases it’s more than just a preference; the raw data may actually too sensitive to share. Both of these concerns have kept companies from reaping the benefits of shared insights even as ecosystem partnerships have grown. But that’s where Privacy-Preserving Computation (PPC) comes in.
I drive research on blockchain and distributed ledgers at Accenture Labs, and Kirby Linvill has led the Labs’ PPC research efforts. We’ve worked together to develop a demonstration of what’s possible with these approaches for the financial industry, enabling privacy-preserving technology for dark pools.
“Dark pool” may sound ominous, but it’s just a term for a private securities exchange. If you’re an institutional investor that wants to make a large stock trade, you don’t want news about that trade to leak out before you make it. If the public market hears that you’re looking to sell a million shares of a company, the price could change significantly before your trade is complete. But you can’t find a buyer for stock you want to sell – or a seller for stock you want to buy – without telling the dark pool that you’re interested. And the second you tell people you’re interested in buying or selling, you create the risk of a leak.
We’ve demonstrated a way to let institutional investors participate in dark pools without that risk. Leveraging the latest PPC advancements from some of our partners, we can blindly match offsetting trading positions from different partners in the pool. What does that mean for dark pool investors? They don’t need to disclose confidential information to other market participants, or even to a centralized dark pool operator. In fact, they can’t see the positions or instruments of any other participant; they’re only informed when there’s a match for their current orders. This could allow dark pools to operate efficiently and at high volume, without the risk of leaked confidential information – preventing market manipulation.
Demo Application built by Accenture Labs to showcase Privacy Preserving Trade Matching
The technical heart of this demonstration is a particular PPC technique called secure Multi-Party Computation (MPC). MPC allows multiple parties to run joint computations on private data without revealing that private data to any other party. Competing enterprises can perform mutually beneficial analytics on their shared data, while ensuring their own sensitive raw data is never usable by any of the other companies in the group. These same approaches could be valuable in many spaces, not just finance; you can imagine nations using MPC to run collective analytics on sensitive census data without needing to share the raw underlying data with other nations, for example.
MPC is only one of the revolutionary PPC techniques that Kirby Linvill and I are exploring with our Labs R&D teams. We’re also exploring Homomorphic Encryption and enterprise use of secure enclaves. Homomorphic encryption allows computation on encrypted data without the need to decrypt it first (or at all). On the hardware wide, secure enclaves are special modules that allow for data processing within hardware-provided, encrypted private memory areas directly on the microprocessor chip.
Privacy-preserving computational techniques offer major value for companies across industries. Whether it’s banking, healthcare, or manufacturing, the privacy of sensitive information is key, but there is significant value to be gained from data collaboration. We recently published a point of view, “Maximize collaboration through secure data sharing,” where we discuss privacy-preserving techniques and the related business opportunities they support. We’ve also recently released an Open Source project called PyHeal to help make Microsoft SEAL, a homomorphic encryption framework, more broadly accessible. Check out our PoV, and stay tuned to learn more as we continue to unlock privacy-preserving computing at scale!
For more information on our trusted distributed computing research agenda, contact Giuseppe Giordano and Kirby Linvill.