58 citations · 168 across the 11 of their papers we have counts for
12 papers
A Large-Scale Evaluation of Cookie Notice Privacy and Accessibility for Blind and Low Vision Users
James M. Clarke, Maryam Mehrnezhad
We present the first large-scale privacy and accessibility evaluation of cookie notices across 2,061 domains for blind and low vision (BLV) users. Using a Playwright-based crawler,…
Measuring Online Hate on 4chan using Pre-trained Deep Learning Models
Adrian Bermudez-Villalva, Maryam Mehrnezhad, Ehsan Toreini
Online hate speech can harmfully impact individuals and groups, specifically on non-moderated platforms such as 4chan where users can post anonymous content. This work focuses on a…
Perceptual Hash Inversion Attacks on Image-Based Sexual Abuse Removal Tools
Sophie Hawkes, Christian Weinert, Teresa Almeida +1
We show that perceptual hashing, crucial for detecting and removing image-based sexual abuse (IBSA) online, faces vulnerabilities from low-budget inversion attacks based on generat…
The Importance of Collective Privacy in Digital Sexual and Reproductive Health
Teresa Almeida, Maryam Mehrnezhad, Stephen Cook
There is an abundance of digital sexual and reproductive health technologies that presents a concern regarding their potential sensitive data breaches. We analyzed 15 Internet of T…
Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems
Ehsan Toreini, Maryam Mehrnezhad, Aad van Moorsel
Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to comp…
A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards
Joshua Harrison, Ehsan Toreini, Maryam Mehrnezhad
With recent developments in deep learning, the ubiquity of micro-phones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat…
Invisible, Unreadable, and Inaudible Cookie Notices: An Evaluation of Cookie Notices for Users with Visual Impairments
James M. Clarke, Maryam Mehrnezhad, Ehsan Toreini
This paper investigates the accessibility of cookie notices on websites for users with visual impairments (VI) via a set of system studies on top UK websites (n=46) and a user stud…
"My sex-related data is more sensitive than my financial data and I want the same level of security and privacy": User Risk Perceptions and Protective Actions in Female-oriented Technologies
Maryam Mehrnezhad, Teresa Almeida
The digitalization of the reproductive body has engaged myriads of cutting-edge technologies in supporting people to know and tackle their intimate health. Generally understood as…
"I feel invaded, annoyed, anxious and I may protect myself": Individuals' Feelings about Online Tracking and their Protective Behaviour across Gender and Country
Kovila P. L. Coopamootoo, Maryam Mehrnezhad, Ehsan Toreini
Online tracking is a primary concern for Internet users, yet previous research has not found a clear link between the cognitive understanding of tracking and protective actions. We…
DOMtegrity: Ensuring Web Page Integrity against Malicious Browser Extensions
Ehsan Toreini, Maryam Mehrnezhad, Siamak F. Shahandashti +1
In this paper, we address an unsolved problem in the real world: how to ensure the integrity of the web content in a browser in the presence of malicious browser extensions? The pr…
Stealing PINs via Mobile Sensors: Actual Risk versus User Perception
Maryam Mehrnezhad, Ehsan Toreini, Siamak F. Shahandashti +1
In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-b…
TouchSignatures: Identification of User Touch Actions and PINs Based on Mobile Sensor Data via JavaScript
Maryam Mehrnezhad, Ehsan Toreini, Siamak F. Shahandashti +1
Conforming to W3C specifications, mobile web browsers allow JavaScript code in a web page to access motion and orientation sensor data without the user's permission. The associated…