Multi-Party Computation (MPC) is a cryptographic technique that first emerged back in the 1980s. For many years, it remained theoretical with hypothetical scenarios. That all changed in 2008, when the world’s first commercial implementation of Multi-Party Computation took place called The Danish Sugar Beet Auction.
When the European Union drastically reduced the support for sugar beet production, a double auction was needed for setting the price of sugar beets in Denmark. MPC was used to run the auction, keeping the farmers’ bids confidential while still ensuring a fair outcome.
One of the people behind this breakthrough was Ivan Damgård, Partisia’s co-founder and the godfather of MPC.
This successful demonstration opened the door for real-world applications of MPC, proving that it could move beyond theory and solve real-world business problems involving private data that should be kept confidential.
Background: Why sugar beets?
Denmark has a significant sugar beet industry with around 4000 sugar beet farmers. All of them are growing beets for the company Danisco, a Danish producer of ingredients for food and other consumer products.
Each farmer has a contract specifying how many beets they can produce and the price at which Danisco will purchase them. Although these contracts can be traded internally between farmers, the process has traditionally relied on limited, one-on-one negotiations.
A major turning point came when the European Union significantly reduced its financial support for sugar beet production. This put immediate pressure on farmers to reallocate contracts to farmers where production pays off best.
Finding the market clearing price – the point where demand meets supply most effectively – became crucial.
It was realized that this was best done via a double auction.
A double auction for sugar beets: Who can be trusted?
A double auction is a process where buyers and sellers simultaneously submit bids (offers to buy) and asks (offers to sell) for a product or service.
This method was chosen for market-clearing pricing because, in any industry, whether it’s cars, furniture, or artwork, pricing and bids are most effectively managed through an auctioneer. An auction system ensures fair competition, transparency, and efficiency, making it the optimal approach for determining market prices.
However, the question arose: Who should act as the auctioneer?
Danisco? No, because bids reveal private information about a farmer's economy, which could be misused by Danisco.
The farmers themselves? Also no. Farmers do not fully trust each other, and Danisco wants some control since contracts can act as security for debt to Danisco.
An external consulting firm? Well, then everything depends on this one party keeping data confidential and their tools working smoothly.
One that would protect each farmer’s confidential information while still determining fair prices and optimal outcomes for all.
What to do when no single party can be trusted with sensitive data? In comes Multi-Party Computation (MPC), a technology that removes the need for a middleman.
And so… the stage was set for the groundbreaking use of MPC in the Danish Sugar Beet Auction.
The solution: A virtual auctioneer using Multi-Party Computation
The solution was to use a “virtual auctioneer”.
The role of the auctioneer was played by Multi-Party Computation with the three parties:
Danisco
DKS (the Danish sugar beet growers association)
The SIMAP project (Secure Information Management and Processing, a project sponsored by the Danish National Research Agency).
A three party solution was selected, partly because it was natural in the given scenario (with three main stakeholder groups), but also because it allowed using efficient information theoretic tools such as secret sharing.
It allows multiple stakeholders, who may have competing interests, to compute a result together without exposing their individual inputs.
The bid interface used in the Danish Sugar Beet Auction 2008, the world's first practical implementation of Multi-Party Computation (MPC).
Why use Multi-Party Computation (MPC) for the double auction?
No party sees all the data. With Multi-Party Computation, nobody gets to see the bids in an unencrypted form. This removes the need for any one party to take full responsibility of keeping the information private.
Privacy is ensured. Since the data is split and processed in a way that no one has access to it all at once, sensitive information – the individual bid amounts – is protected from being exposed.
Easier collaboration among conflicting interests. Different stakeholders often have clashing goals. MPC offers a built-in way for them to work together securely without having to negotiate a complicated security plan.
One secure hardware device isn’t enough. Relying on a single piece of hardware creates a single point of trust (and possible failure), forcing everyone to depend on its flawless operation. With MPC, the process doesn’t depend on just one device.
The market clearing price was found using Multi-Party Computation (MPC)
The Danish Sugar Beet Auction successfully used Multi-Party Computation (MPC) to determine the market clearing price. Three servers performed the calculation on encrypted bids that were never decrypted, ensuring no one could see the farmers’ individual bids.
A survey among farmers after the auction highlighted its positive impact:
81% found that MPC simplified the process of trading contracts.
78% said confidentiality of bids was important.
86% were satisfied with the level of confidentiality provided.
These findings show that privacy was an important factor for the farmers when using MPC to find the market clearing price in the Danish sugar beet market.
And you know what? All parties were extremely happy with the solution, so the auction ran every year until 2015, when it was no longer needed.
What can we learn from The Danish Sugar Beet Auction?
The Danish Sugar Beet Auction proved that Multi-Party Computation (MPC) can be applied successfully in real-world business scenarios.
The auction showed that it’s possible to combine private information from many participants without ever revealing anyone’s individual data by using MPC. This breakthrough created opportunities for collaboration even when participants had conflicting interests and sensitive information that needs to be kept safe – challenges that often block innovation and growth.
Curious to dive deeper into this groundbreaking, first large-scale, commercial use of Multi-Party Computation? Read the whitepaper co-authored by Ivan Damgård and others.
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