Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Christian Weinert"'
Autor:
Christoph Hagen, Christian Weinert, Christoph Sendner, Alexandra Dmitrienko, Thomas Schneider
Publikováno v:
ACM Transactions on Privacy and Security (TOPS)
Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigati
Publikováno v:
Applied Cryptography and Network Security-19th International Conference, ACNS 2021, Kamakura, Japan, June 21–24, 2021, Proceedings, Part II
Applied Cryptography and Network Security ISBN: 9783030783747
ACNS (2)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Applied Cryptography and Network Security
Applied Cryptography and Network Security ISBN: 9783030783747
ACNS (2)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Applied Cryptography and Network Security
Multi-party computation (MPC) allows two or more parties to jointly and securely compute functions over private inputs. Cryptographic protocols that realize MPC require functions to be expressed as Boolean or arithmetic circuits. Deriving such circui
Autor:
Christian Weinert, Alexandra Dmitrienko, Thomas Schneider, Christoph Sendner, Christoph Hagen
Publikováno v:
NDSS
Proceedings 2021 Network and Distributed System Security Symposium
Proceedings 2021 Network and Distributed System Security Symposium
Publikováno v:
Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks
WISEC
WISEC
Apple's file-sharing service AirDrop leaks phone numbers and email addresses by exchanging vulnerable hash values of the user's own contact identifiers during the authentication handshake with nearby devices. In a paper presented at USENIX Security'2
Publikováno v:
PPMLP@CCS
Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice
Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice
The ubiquitous deployment of machine learning (ML) technologies has certainly improved many applications but also raised challenging privacy concerns, as sensitive client data is usually processed remotely at the discretion of a service provider. The
Publikováno v:
24. European Conference on Artificial Intelligence (ECAI'20)
Publikováno v:
CCSW@CCS
Secure function evaluation (SFE) allows two parties to jointly evaluate a publicly known function without revealing their respective inputs. SFE can be realized via well-known cryptographic protocols, such as Yao's garbled circuits (GC) and the proto
Autor:
Christian Weinert
Publikováno v:
Voluntaris. 5:159-162
Autor:
Ferdinand Brasser, Tommaso Frassetto, Ahmad-Reza Sadeghi, Korbinian Riedhammer, Thomas Schneider, Christian Weinert
Publikováno v:
INTERSPEECH