Secure Multiparty Computation in Sugarbeet Auctions

1. Secure Multiparty Computation goes Live Peter Bogetoft, Dan Christensen, Ivan Damgård, Martin Geisler, Thomas Jakobsen, Mikkel Krøigaard, Janus Nielsen, Jesper Nielsen, Kurt Nielsen, Jacob Pagter, Michael Schwartzbach, Tomas Toft Barbados, 2009 Ivan Damgård Århus University

2. January 14, 2008 • 1200 Danish Farmers trade production rights for sugarbeets via an electronic auction. • As a result, 25.000 tons worth of production rights change owner • The market price at which trading occurs is computed by 3 servers, based on encrypted bids that are never decrypted. • First large-scale application of Secure Multiparty Computation.

3. Some Background • About 5000 Danish farmers produce sugarbeets. A farmer owns a contract, a production right, allowing him to produce a certain quantity of beets. • Beets are delivered to Danisco, the only Danish sugar producer. • Recently EU drastically reduced support for production, as a result urgent need to move production to places where it pays best. • Hence need for a nation wide-market where production rights can be traded. • Decision: use a double auction.

4. Double Auction Some commodity is traded. Many sellers, many buyers. Each bid from a seller is a list of numbers: ”At each possible price per unit, I want to sell this much” – similar for buyers. Send to auctioneer. For each possible price, auctioneer add up bids to find total supply, and total demand in the market at that price. quantity supply demand price/unit Market clearing price Demand goes down, supply up with increasing price, so can find solution by binary search: number of comparisons logarithmic in number of prices. Everybody gets to sell or buy the quantity they were willing to sell or buy at the mcp.

5. Privacy of Bids – Who should be auctioneer? In the sugar beet case, privacy of bids is crucial. Bids reveal private information about a farmer’s economy. In survey, 75% said it was important to them that bids were confidential. Information on farmers’ ecomic situation can be misused by Danisco  Danisco cannot be auctioneer Farmers could do it themselves? No, Danisco wants some control, some farmer’s owe Danisco money, contracts act as security. Besides, farmers do not all trust each other.. Hire a consultancy house to be auctioneer? No, too expensive Solution chosen: do an electronic double auction, with ”virtual auctioneer”, implemented by Danisco, DKS (farmer’s organization) and the SIMAP research project, using multiparty computation.

6. Multiparty computation, intuition • m input clients I1,...,Im and n servers P1, P2, …, Pn • Usually m large, n small (ex. m=1500, n=3) • Player Ii submits input xi in appropriately encrypted form • By collaborating, P1,..Pn compute y= f(x1,…,xm) for some function f. Must be be done securely, i.e., • All learn the correct value of y • Nothing except y is made public. In particular no input is decrypted, and no Pi gets access to any information on the inputs. • P1,.., Pn implement a ”virtual trusted party”

7. Computation needed • Lots of additions to find total demand/supply at each price. • Some comparisons to do binary search for market clearing price. • Comparison results can be public, follow anyway from value of market clearing price. • Protocols Used • Based on Shamir Secret sharing modulo a sufficiently large prime p (65 bits in our case). Notation: [c] means c, secret shared. • Addition and multiplication easy using standard tools • Comparison a bit harder..

8. Comparison ct’d Problem: hard to go from [c] to [c0], [c1],…, [cn] where [ci] is i’th bit of c. Observation [from Damgård et al TCC 07] : much easier go from [c0], [c1],…, [cn] to [c] since Shamir secret sharing is linear - just multiply by 2-powers and add. Suppose we know a,b are at most t-bit numbers. Algorithm 1. Generate random shared 0/1 values [r0],…., [rv], compute [r], where r= r0+ 2 r1 + … + 2vrv v chosen so r >> a,b 2. Compute and open T= r + 2t +a – b (statistically secure since r >> a,b). 3. Remaining problem: if we subtract bit-wise shared number r from the public T, will bit no. t be set? Hence enough to do a linear (in t) number of binary operations. In paper: technique to do this in logarithmic number of rounds.

9. How to deliver inputs The obvious idea: client secret shares each input number and encrypts all shares intended for Pi under his public key Communication becomes linear size in number of servers, to encrypt B bits, must send O(nB) bits In paper: Technique to encrypt shares such that only an additive overhead depends on number of servers, need to send O(n) + O(B) bits.

10. Implementation Architecture SIMAP Danisco SIMAP web-server DB session LAN log-in Danisco Java-applet Encrypted bid DKS farmer Submitting bids beregning

11. Implementation Architecture SIMAP Danisco SIMAP web-server DB DB session LAN log-in DB Danisco Java-applet Encrypted bid DB DKS Farmer Submitting bids Computation

12. Performance 1

13. Performance 2

14. Why Multiparty Computation? • If some party has access to the private data in the clear, everone has to agree on a security policy: who is allowed access, and when? Who is held responsible if data leaks and what are the consequences? • Difficult negotiations because parties have conflicting interests, could have halted the entire project. • Nobody wanted the responsibility of having to store the bids in clear. These concerns seem more important than fear of malicious attack by the “opponent” => security against malicious attacks seems less important. • With multiparty computation, bids are kept protected at all times, hence easier to communicate security policy to the farmers. • An MPC based software solution can be developed and scrutinized once and cost can be amortized over several applications. In contrast, having a consultancy house be trusted party costs a fee every time.

15. Use secure hardware instead? This is also a single trusted party solution! Even if hardware protection cannot be broken, security depends on how the hardware box is administrated, maintained, backed up etc. Hence we still need to agree on a common security policy. Seems more natural for players to use hardware to improve their own security

16. In Conclusion.. Applications of MPC can be realized with the efficiency and functionality that is required in real life. Ability of MPC to keep private data secret to everyone, really is useful in practice. More applications? I think yes, but give it time! Compare to Digital signatures: invented in 1977, first nation-wide system in Denmark started 2 years ago! More info on research projects, see www.sikkerhed.alexandra.dk/uk/projects/simap.htm New spin-off by the SIMAP people www.partisia.com

17. Attacks External attacks: need to handle those, but similar issues and solutions as lots of other applications.. Attacks by bidders Can protect against malicious bidders, we decided the risk was too low to motivate cost. Attacks by parties doing the computation No party seriously suspected others of a malicious attack, BUT not acceptable to give all the bids to the opponent AND nobody wanted the responsibility of dealing with the private data. Would lead to earlier mentioned problems with agreeing on security policy etc. Hence we went for semi-honest security.