Private Multi-party Matrix Multiplication and Trust Computations
This addresses privacy-preserving computations for multi-party scenarios like trust evaluation in networks, though it is incremental with protocol improvements and security enhancements.
The paper tackles secure distributed matrix multiplication where each participant owns one row of both matrices and wants to learn one distinct row of the product without revealing inputs, improving a weighted average protocol for dot-product computation and proposing a five-round protocol using homomorphic encryption that is secure against semi-honest or malicious adversaries with verified security via ProVerif and countermeasures against attacks.
This paper deals with distributed matrix multiplication. Each player owns only one row of both matrices and wishes to learn about one distinct row of the product matrix, without revealing its input to the other players. We first improve on a weighted average protocol, in order to securely compute a dot-product with a quadratic volume of communications and linear number of rounds. We also propose a protocol with five communication rounds, using a Paillier-like underlying homomorphic public key cryptosystem, which is secure in the semi-honest model or secure with high probability in the malicious adversary model. Using ProVerif, a cryptographic protocol verification tool, we are able to check the security of the protocol and provide a countermeasure for each attack found by the tool. We also give a randomization method to avoid collusion attacks. As an application, we show that this protocol enables a distributed and secure evaluation of trust relationships in a network, for a large class of trust evaluation schemes.