Secure Multi-Party Computation Against Passive Adversaries

This book focuses on multi-party computation (MPC) protocols in the passive corruption model (also known as the semi-honest or honest-but-curious model). The authors present seminal possibility and feasibility results in this model and includes formal security proofs. Even though the passive corruption model may seem very weak, achieving security against such a benign form of adversary turns out to be non-trivial and demands sophisticated and highly advanced techniques. MPC is a fundamental concept, both in cryptography as well as distributed computing. On a very high level, an MPC protocol allows a set of mutually-distrusting parties with their private inputs to jointly perform any computation on their inputs. Examples of such computation include, but not limited to, privacy-preserving data mining; secure e-auction; private set-intersection; and privacy-preserving machine learning. MPC protocols emulate the role of an imaginary, centralized trusted third party (TTP) that collects the inputs of the parties, performs the desired computation, and publishes the result. Therefore, any task that can be carried out securely in the presence of a centralized TTP can be performed by decentralizing the role of the TTP by running an MPC protocol among the parties themselves. Due to its powerful abstraction, the MPC problem has been widely studied, both by the cryptography community as well as the distributed computing community, over the past four decades.