TY - GEN

T1 - Jointly private convex programming

AU - Hsu, Justin

AU - Huang, Zhiyi

AU - Roth, Aaron

AU - Wu, Zhiwei Steven

PY - 2016

Y1 - 2016

N2 - We present a general method for approximately solving convex programs defined by private information from agents, when the solution can be naturally partitioned among the agents. This class of problems includes multi-commodity flow problems, general allocation problems, and multi-dimensional knapsack problems, among other examples. The accuracy of our algorithm depends on the number of coupling constraints, which bind multiple agents. On the other hand, our accuracy is nearly independent of the number of variables, and in many cases, actually improves as the number of agents increases. A special case of our result (solving general allocation problems beyond "Gross Substitute" preferences) resolves the main open problem from [Hsu et al. STOC 2014]. We also consider strategic agents who have preferences over their part of the solution. For any convex program in our class that maximizes social welfare, we show how to create an approximately dominant strategy truthful mechanism, approximately maximizing welfare. The central idea is to charge agents prices based on the approximately optimal dual variables, which are themselves computed under differential privacy. Our results substantially broaden the class of problems that are known to be solvable under privacy and/or incentive constraints.

AB - We present a general method for approximately solving convex programs defined by private information from agents, when the solution can be naturally partitioned among the agents. This class of problems includes multi-commodity flow problems, general allocation problems, and multi-dimensional knapsack problems, among other examples. The accuracy of our algorithm depends on the number of coupling constraints, which bind multiple agents. On the other hand, our accuracy is nearly independent of the number of variables, and in many cases, actually improves as the number of agents increases. A special case of our result (solving general allocation problems beyond "Gross Substitute" preferences) resolves the main open problem from [Hsu et al. STOC 2014]. We also consider strategic agents who have preferences over their part of the solution. For any convex program in our class that maximizes social welfare, we show how to create an approximately dominant strategy truthful mechanism, approximately maximizing welfare. The central idea is to charge agents prices based on the approximately optimal dual variables, which are themselves computed under differential privacy. Our results substantially broaden the class of problems that are known to be solvable under privacy and/or incentive constraints.

UR - http://www.scopus.com/inward/record.url?scp=84963542261&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963542261&partnerID=8YFLogxK

U2 - 10.1137/1.9781611974331.ch43

DO - 10.1137/1.9781611974331.ch43

M3 - Conference contribution

AN - SCOPUS:84963542261

T3 - Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

SP - 580

EP - 599

BT - 27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016

A2 - Krauthgamer, Robert

PB - Association for Computing Machinery

T2 - 27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016

Y2 - 10 January 2016 through 12 January 2016

ER -