A new polynomial-time algorithm for linear programming
Combinatorica
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Privacy preserving auctions and mechanism design
Proceedings of the 1st ACM conference on Electronic commerce
A study of several specific secure two-party computation problems
A study of several specific secure two-party computation problems
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
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
Solving Linear Programs Using Multiparty Computation
Financial Cryptography and Data Security
Secure multiparty linear programming using fixed-point arithmetic
ESORICS'10 Proceedings of the 15th European conference on Research in computer security
Efficient distributed linear programming with limited disclosure
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
On the (Im)possibility of privately outsourcing linear programming
Proceedings of the 2013 ACM workshop on Cloud computing security workshop
Secure and efficient distributed linear programming
Journal of Computer Security - DBSec 2011
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Linear programming is one of maths' greatest contributions to industry. There are many places where linear programming could be beneficially applied across more than one company, but there is a roadblock. Companies have secrets. The data needed for joint optimization may need to be kept private either through concerns about leaking competitively sensitive data, or due to privacy legislation. Recent research has tackled the problem of privacy-preserving linear programming. One appealing group of approaches uses a 'disguising' transformation to allow one party to perform the joint optimization without seeing the secret data of the other parties. These approaches are very appealing from the point of view of simplicity, efficiency, and flexibility, but we show here that all of the existing transformations have a critical flaw.