"Say I'm in public...I don't want my nudes to pop up." User Threat Models for Using Vault Applications

Chris Geeng, Natalie Chen, Kieron Ivy Turk, Jevan Hutson, Damon McCoy

Presented at : SOUPS Symposium 2024

Published in Proceedings of the Twentieth Symposium on Usable Privacy and Security (SOUPS 2024).

The official publication is available here.

Many users share access to their devices with friends and partners, but may have content that they do not wish others to see. One solution to this are Vault Apps: tools which encrypt and hide media stored on a user’s device, often behind a PIN or other secondary security mechanism. Prior research analysed the security of these applications, but not what users need from them, and which threat models they are protecting against.

We interview 18 users of vault apps to better understand what content is stored in vault apps and against which adversaries users are protecting themselves. We define a set of threat models for vault apps and highlight which are the most relevant, and explore designs which can best protect users against these threat actors.

Can’t Keep Them Away: The Failures of Anti-stalking Protocols in Personal Item Tracking Devices

Kieron Ivy Turk, Alice Hutchings, Alastair R Beresford

Presented at : Security Protocols Workshop 2023

Published in Lecture Notes in Computer Science, vol 14186 (Security Protocols XXVIII).

T official publication is available here, and the open-access version is available here alongside the transcript of discussion.

A number of technology companies have introduced personal item tracking devices to allow people to locate and keep track of items such as keys and phones. However, these devices are not always used for their intended purpose: they have been used in cases of domestic abuse and stalking to track others without their consent.

In response, manufacturers introduced a range of anti-stalking features designed to detect and mitigate misuse of their products. In this paper, we explore common implementations of these anti-stalking features and analyse their limitations. In other research, we identified that very few people use anti-stalking features, even when they know that someone might be tracking them and are incentivised to evade them.

We additionally identify several failures of the features that prevent them from performing their intended purpose even if they were in use. It is impossible for anti-stalking features to identify the difference between ‘bad’ tracking and ‘good’ tracking. Furthermore, some features work on some types of phones for some types of tracking devices, but not all work on all phones for all trackers. Some anti-stalking features are not enabled by default, and some require manual intervention to scan for devices. We provide suggestions for how these features could be improved, as well as ideas for additional anti-stalking features that could help mitigate the issues discussed in this paper.

This is one of two studies I have published on item tracker stalking; the sister study is “Stop Following Me”, described above.

A tight scrape: methodological approaches to cybercrime research data collection in adversarial environments.

Kieron Turk, Sergio Pastrana, Ben Collier

Presented at WACCO 2020 : Second Workshop on Attackers and Cyber-Crime Operations

Published in the proceedings of IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) 2020.

My blog post presenting this paper is available here, and the official publication in IEEE is available here.

A study of “adversarial scraping” for academic research, in which websites implement assorted defences to prevent web scraping while researchers attempt to bypass these defences to collect data for futher investigation. Inspired by the novel “attacks on the browser” encountered on a small collection of sites, we decided to document the range of features which we have encountered through assorted scraping projects, as well as the countermeasures that can be used to overcome these methods.

We then classify the defences by effectiveness, and find that a large number of the methods used to prevent crawling are inneffective, or of minimal impact. Other systems successfully slow down crawling, inhibiting historical data collection, while a small number of defences are capable of preventing scraping entirely in certain circumstances.

We identify two environments which can be analysed independently: sites hosted on Tor (“onions”) and chat channels. Onions are generally easier to scrape, due to many defences being unusable with the associated privacy restrictions (such as Javascript being near-universally disabled). Chat channels such as those on Discord and Telegram use far fewer technical measures to prevent scraping; instead, many use moderator intervention to identify and block automated accounts that are identified as bots.