Sprache: Englisch
Verlag: Cambridge University Press, 2015
ISBN 10: 1107043050 ISBN 13: 9781107043053
Anbieter: medimops, Berlin, Deutschland
Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
Sprache: Englisch
Verlag: Cambridge University Press, 2015
ISBN 10: 1107043050 ISBN 13: 9781107043053
Anbieter: WorldofBooks, Goring-By-Sea, WS, Vereinigtes Königreich
EUR 53,10
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Sprache: Englisch
Verlag: Cambridge University Press, 2015
ISBN 10: 1107043050 ISBN 13: 9781107043053
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 89,59
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Cambridge University Press, 2015
ISBN 10: 1107043050 ISBN 13: 9781107043053
Anbieter: Kennys Bookstore, Olney, MD, USA
EUR 127,97
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. This book provides information on theoretically secure multiparty computation (MPC) and secret sharing, and the fascinating relationship between the two concepts. Num Pages: 381 pages, 9 b/w illus. 41 exercises. BIC Classification: UR. Category: (U) Tertiary Education (US: College). Dimension: 263 x 186 x 26. Weight in Grams: 862. . 2015. Hardcover. . . . . Books ship from the US and Ireland.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 130,96
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 381 pages. 10.50x7.50x1.00 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2015
ISBN 10: 1107043050 ISBN 13: 9781107043053
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved This text is the first to present a comprehensive treatment of unconditionally secure techniques for multiparty computation (MPC) and secret sharing. In a secure MPC, each party possesses some private data, while secret sharing provides a way for one party to spread information on a secret such that all parties together hold full information, yet no single party has all the information. The authors present basic feasibility results from the last 30 years, generalizations to arbitrary access structures using linear secret sharing, some recent techniques for efficiency improvements, and a general treatment of the theory of secret sharing, focusing on asymptotic results with interesting applications related to MPC.