Multi-party Computation: Privacy in Coopetition-Reference-Cited by-同舟云学术

Multi-party Computation: Privacy in Coopetition

Published:2024 Issue: Volume: Page:141-155
ISSN:2730-6623
Container-title:Technology, Work and Globalization
language:en
Short-container-title:

Author:

Conway Daniel,Garimella Kiran K.

Abstract

AbstractWhile it may not always be possible to choose who follows you on social media or who gains access to your search history, Multi-Party Computation (MPC) empowers users to select their friends. Privacy environments often approach privacy as a binary property, but MPC offers more privacy granularity, a choice of when to participate, and the ability to initiate a session with a selection of peers. Also, privacy protection occurs at a transaction level and can change with every engagement. MPC offers a way to obtain specific information from friends while preserving the privacy of the data and/or the connection of data to the owner.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-51063-2_7

Reference10 articles.

1. Archer, D. W., Bogdanov, D., Lindell, Y., Kamm, L., Nielsen, K., Pagter, J. I., Smart, N. P., & Wright, R. N. (2018). From Keys to Databases—Real-World Applications of Secure Multi-Party Computation. The Computer Journal, 61, 1749–1771.

2. Bermúdez, J. (2016). A Practical Multi-party Computation Algorithm for a Secure Distributed Online Voting System. ArXiv, abs/1603.04228.

3. Cramer, R., Damgard, I., & Nielsen, J. (2015). Secure Multiparty Computation and Secret Sharing. Cambridge University Press.

4. Dagnino, G., & Padula, G. (2002). Coopetition Strategy: Towards a New Kind of Interfirm Dynamics for Value Creation. EURAM 2nd Annual Conference, Stockholm School of Entrepreneurship, Sweden.

5. Goldreich, O., Micali, S., & Wigderson, A. (1987). How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority. STOC, 1987, 218–229.