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Secure Multi-Party Computation Against Passive Adversaries (Synthesis Lectures on Distributed Computing Theory) - Hardcover

 
9783031121630: Secure Multi-Party Computation Against Passive Adversaries (Synthesis Lectures on Distributed Computing Theory)

Synopsis

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 and securely 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. Due to its powerful abstraction, the MPC problem has been widely studied over the last four decades.

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About the Author

Ashish Choudhury, Ph.D., is an Associate Professor at the International Institute of Information Technology, Bangalore, India. He received his M.S. and Ph.D. degrees from the Indian Institute of Technology, Madras, India. His research interests include theoretical cryptography, with specialization in cryptographic protocols.
 
Arpita Patra, Ph.D., is an Associate Professor at the Indian Institute of Science. She received her Ph.D from the Indian Institute of Technology and held post-doctoral positions at the University of Bristol, ETH Zurich, and Aarhus University. Her research interests include cryptography, with a focus on theoretical and practical aspects of secure multiparty computation protocols.

From the Back Cover

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 and securely 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. Due to its powerful abstraction, the MPC problem has been widely studied over the last four decades.

In addition, this book:
  • Includes detailed security proofs for seminal protocols and state-of-theart efficiency improvement techniques
  • Presents protocols against computationally bounded as well as computationally unbounded adversaries
  • Focuses on MPC protocols in the passive corruption model, presents seminal possibility and feasibility results, and features companion video lectures

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Other Popular Editions of the Same Title

9783031121661: Secure Multi-Party Computation Against Passive Adversaries (Synthesis Lectures on Distributed Computing Theory)

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ISBN 10:  303112166X ISBN 13:  9783031121661
Publisher: Springer, 2023
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 248 pp. Englisch. Seller Inventory # 9783031121630

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Buch. Condition: 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. Seller Inventory # 9783031121630

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