Matthew Gregoire (University of North Carolina at Chapel Hill), Margaret Pierce (University of North Carolina at Chapel Hill), Saba Eskandarian (University of North Carolina at Chapel Hill)

The fast-paced development and deployment of private messaging applications demands mechanisms to protect against the concomitant potential for abuse. While widely used end-to-end encrypted (E2EE) messaging systems have deployed mechanisms for users to verifiably report abusive messages without compromising the privacy of unreported messages, abuse reporting schemes for systems that additionally protect message metadata are still in their infancy. Existing solutions either focus on a relatively small portion of the design space or incur much higher communication and computation costs than their E2EE brethren.

This paper introduces new abuse reporting mechanisms that work for any private messaging system based on onion encryption. This includes low-latency systems that employ heuristic or opportunistic mixing of user traffic, as well as schemes based on mixnets. Along the way, we show that design decisions and abstractions that are well-suited to the E2EE setting may actually impede security and performance improvements in the metadata-hiding setting. We also explore stronger threat models for abuse reporting and moderation not explored in prior work, showing where prior work falls short and how to strengthen both our scheme and others'—including deployed E2EE messaging platforms—to achieve higher levels of security.

We implement a prototype of our scheme and find that it outperforms the best known solutions in this setting by well over an order of magnitude for each step of the message delivery and reporting process, with overheads almost matching those of message franking techniques used by E2EE encrypted messaging apps today.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 118 ) ) ) [post__not_in] => Array ( [0] => 20122 ) )

Evaluating Machine Learning-Based IoT Device Identification Models for Security...

Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

Read More

Secret Spilling Drive: Leaking User Behavior through SSD Contention

Jonas Juffinger (Graz University of Technology), Fabian Rauscher (Graz University of Technology), Giuseppe La Manna (Amazon), Daniel Gruss (Graz University of Technology)

Read More

Starshields for iOS: Navigating the Security Cosmos in Satellite...

Jiska Classen (Hasso Plattner Institute, University of Potsdam), Alexander Heinrich (TU Darmstadt, Germany), Fabian Portner (TU Darmstadt, Germany), Felix Rohrbach (TU Darmstadt, Germany), Matthias Hollick (TU Darmstadt, Germany)

Read More

“Do We Call Them That? Absolutely Not.”: Juxtaposing the...

Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Luca Favaro (Technical University of Munich), and Florian Matthes (Technical University of Munich)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)