output
20172026
most citedFactuality Challenges in the Era of Large Language Models

163 citations

Showing 2022Show all

6 papers · 1 filter

cs.CR2022★ 36 cited

A World Full of Privacy and Security (Mis)conceptions? Findings of a Representative Survey in 12 Countries

Franziska Herbert, Steffen Becker, Leonie Schaewitz +5

Misconceptions about digital security and privacy topics in the general public frequently lead to insecure behavior. However, little is known about the prevalence and extent of suc…

cs.CR2022★ 10 cited

Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups

Franziska Herbert, Steffen Becker, Annalina Buckmann +6

This paper investigates the digital security experiences of four at-risk user groups in Germany, including older adults (70+), teenagers (14-17), people with migration backgrounds,…

cs.CR2022★ 2 cited

DeepSec: Deciding Equivalence Properties for Security Protocols -- Improved theory and practice

Vincent Cheval, Steve Kremer, Itsaka Rakotonirina

Automated verification has become an essential part in the security evaluation of cryptographic protocols. In this context privacy-type properties are often modelled by indistingui…

quant-ph2022★ 11 cited

Quantum Depth in the Random Oracle Model

Atul Singh Arora, Andrea Coladangelo, Matthew Coudron +3

We give a comprehensive characterization of the computational power of shallow quantum circuits combined with classical computation. Specifically, for classes of search problems, w…

cs.CY2022★ 34 cited

(Re)Politicizing Digital Well-Being: Beyond User Engagements

Niall Docherty, Asia J. Biega

The psychological costs of the attention economy are often considered through the binary of harmful design and healthy use, with digital well-being chiefly characterised as a matte…

cs.LG2022★ 5 cited

CECILIA: Comprehensive Secure Machine Learning Framework

Ali Burak Ünal, Nico Pfeifer, Mete Akgün

Since ML algorithms have proven their success in many different applications, there is also a big interest in privacy preserving (PP) ML methods for building models on sensitive da…