Sharing Decryption in the Context of Voting or Lotteries
FC '00 Proceedings of the 4th International Conference on Financial Cryptography
An Unconditionally Secure Protocol for Multi-Party Set Intersection
ACNS '07 Proceedings of the 5th international conference on Applied Cryptography and Network Security
ACNS '07 Proceedings of the 5th international conference on Applied Cryptography and Network Security
Information Theoretically Secure Multi Party Set Intersection Re-visited
Selected Areas in Cryptography
CANS '09 Proceedings of the 8th International Conference on Cryptology and Network Security
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Proceedings of the first ACM conference on Data and application security and privacy
Fair and privacy-preserving multi-party protocols for reconciling ordered input sets
ISC'10 Proceedings of the 13th international conference on Information security
(If) size matters: size-hiding private set intersection
PKC'11 Proceedings of the 14th international conference on Practice and theory in public key cryptography conference on Public key cryptography
Privacy-preserving set operations
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
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Privacy-preserving reconciliation protocols on ordered sets are protocols that solve a particular subproblem of secure multiparty computation. Here, each party holds a private input set of equal size in which the elements are ordered according to the party's preferences. The goal of a reconciliation protocol on these ordered sets is then to find all common elements in the parties' input sets that maximize the joint preferences of the parties. In this paper, we present two main contributions that improve on the current state of the art. First, we propose two new protocols for privacy-preserving reconciliation and prove their correctness and security properties. We implement and evaluate our protocols as well as two previously published multi-party reconciliation protocols. Our implementation is the first practical solution to reconciliation problems in the multi-party setting. Our comparison shows that our new protocols outperform the original protocols. The basic optimization idea is to reduce the highest degree polynomial in the protocol design. Second, we generalize privacy-preserving reconciliation protocols, i. e., relaxing the input constraint from totally ordered input sets of equal size to pre-ordered input sets of arbitrary size.