Katharina Kohls

Katharina Kohls, Claudia Diaz

We tackle the challenge of reliably determining the geo-location of nodes in decentralized networks, considering adversarial settings and without depending on any trusted landmarks. In particular, we consider active adversaries that control a subset of nodes, announce false locations and strategically manipulate measurements. To address this problem we propose, implement and evaluate VerLoc, a system that allows verifying the claimed geo-locations of network nodes in a fully decentralized manner. VerLoc securely schedules roundtrip time (RTT) measurements between randomly chosen pairs of nodes. Trilateration is then applied to the set of measurements to verify claimed geo-locations. We evaluate VerLoc both with simulations and in the wild using a prototype implementation integrated in the Nym network (currently run by thousands of nodes). We find that VerLoc can localize nodes in the wild with a median error of 60 km, and that in attack simulations it is capable of detecting and filtering out adversarial timing manipulations for network setups with up to 20 % malicious nodes.

Theresa Stadler, Wouter Lueks, Katharina Kohls et al.

In this document, we analyse the potential harms a large-scale deployment of the Luca system might cause to individuals, venues, and communities. The Luca system is a digital presence tracing system designed to provide health departments with the contact information necessary to alert individuals who have visited a location at the same time as a SARS-CoV-2-positive person. Multiple regional health departments in Germany have announced their plans to deploy the Luca system for the purpose of presence tracing. The system's developers suggest its use across various types of venues: from bars and restaurants to public and private events, such religious or political gatherings, weddings, and birthday parties. Recently, an extension to include schools and other educational facilities was discussed in public. Our analysis of the potential harms of the system is based on the publicly available Luca Security Concept which describes the system's security architecture and its planned protection mechanisms. The Security Concept furthermore provides a set of claims about the system's security and privacy properties. Besides an analysis of harms, our analysis includes a validation of these claims.

Lea Schönherr, Katharina Kohls, Steffen Zeiler et al.

Voice interfaces are becoming accepted widely as input methods for a diverse set of devices. This development is driven by rapid improvements in automatic speech recognition (ASR), which now performs on par with human listening in many tasks. These improvements base on an ongoing evolution of DNNs as the computational core of ASR. However, recent research results show that DNNs are vulnerable to adversarial perturbations, which allow attackers to force the transcription into a malicious output. In this paper, we introduce a new type of adversarial examples based on psychoacoustic hiding. Our attack exploits the characteristics of DNN-based ASR systems, where we extend the original analysis procedure by an additional backpropagation step. We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i.e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception. To further minimize the perceptibility of the perturbations, we use forced alignment to find the best fitting temporal alignment between the original audio sample and the malicious target transcription. These extensions allow us to embed an arbitrary audio input with a malicious voice command that is then transcribed by the ASR system, with the audio signal remaining barely distinguishable from the original signal. In an experimental evaluation, we attack the state-of-the-art speech recognition system Kaldi and determine the best performing parameter and analysis setup for different types of input. Our results show that we are successful in up to 98% of cases with a computational effort of fewer than two minutes for a ten-second audio file. Based on user studies, we found that none of our target transcriptions were audible to human listeners, who still understand the original speech content with unchanged accuracy.