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

Choudhury, Ashish; Patra, Arpita

 
9783031121661: Secure Multi-Party Computation Against Passive Adversaries (Synthesis Lectures on Distributed Computing Theory)
Softcover
ISBN 10: 303112166X ISBN 13: 9783031121661
Verlag: Springer, 2023

Inhaltsangabe

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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Über die Autorin bzw. den Autor

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.

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2023
Sprache
Englisch
ISBN 10 
303112166X
ISBN 13 
9783031121661
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
248
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