Integer and combinatorial optimization
Integer and combinatorial optimization
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Privacy-Preserving Cooperative Scientific Computations
CSFW '01 Proceedings of the 14th IEEE workshop on Computer Security Foundations
A study of several specific secure two-party computation problems
A study of several specific secure two-party computation problems
The Effect of Policies for Selecting the Solution of a DisCSP on Privacy Loss
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Privacy-preserving linear programming
Proceedings of the 2009 ACM symposium on Applied Computing
A Secure Revised Simplex Algorithm for Privacy-Preserving Linear Programming
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Hiccups on the road to privacy-preserving linear programming
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Confidentiality preserving integer programming for global routing
Proceedings of the 49th Annual Design Automation Conference
Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud
Journal of Parallel and Distributed Computing
Secure and efficient distributed linear programming
Journal of Computer Security - DBSec 2011
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In today's networked world, resource providers and consumers are distributed globally and locally. However, with resource constraints, optimization is necessary to ensure the best possible usage of such scarce resources. Distributed linear programming (DisLP) problems allow collaborative agents to jointly maximize profits (or minimize costs) with a linear objective function while conforming to several shared as well as local linear constraints. Since each agent's share of the global constraints and the local constraints generally refer to its private limitations or capacities, serious privacy problems may arise if such information is revealed. While there have been some solutions proposed that allow secure computation of such problems, they typically rely on inefficient protocols with enormous communication cost. In this paper, we present a secure and extremely efficient protocol to solve DisLP problems where constraints are arbitrarily partitioned and no variable is shared between agents. In the entire protocol, each agent learns only a partial solution (about its variables), but learns nothing about the private input/output of other agents, assuming semi-honest behavior. We present a rigorous security proof and communication cost analysis for our protocol and experimentally validate the costs, demonstrating its robustness.