secure multi party computation against von patra arpita (5 Ergebnisse)

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 authorspresent seminal possibility and feasibility results in this model and includes formalsecurity 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-trivialand demands sophisticated and highly advanced techniques. MPC is a fundamentalconcept, 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 privateinputs to jointly and securely perform any computation on their inputs. Examples ofsuch computation include, but not limited to, privacy-preserving data mining; securee-auction; private set-intersection; and privacy-preserving machine learning. MPCprotocols emulate the role of an imaginary, centralized trusted third party (TTP) thatcollects the inputs of the parties, performs the desired computation, and publishes theresult. Due to its powerful abstraction, the MPC problem has been widely studied overthe last four decades.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 authorspresent seminal possibility and feasibility results in this model and includes formalsecurity 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-trivialand demands sophisticated and highly advanced techniques. MPC is a fundamentalconcept, 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 privateinputs to jointly and securely perform any computation on their inputs. Examples ofsuch computation include, but not limited to, privacy-preserving data mining; securee-auction; private set-intersection; and privacy-preserving machine learning. MPCprotocols emulate the role of an imaginary, centralized trusted third party (TTP) thatcollects the inputs of the parties, performs the desired computation, and publishes theresult. Due to its powerful abstraction, the MPC problem has been widely studied overthe last four decades.

Kartoniert / Broschiert. Zustand: New.

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