arXiv subjects

Dayana Spagnuelo

Publications and source records attributed to Dayana Spagnuelo.

3 recordsLinked to original sources

arXiv2025

In Pursuit of Privacy: The Value-Centered Privacy Assistant

Many users make quick decisions that affect their data privacy without due consideration of their values. One such decision is whether to download a smartphone app to their device. Previous work has suggested a relationship between values, privacy preferences, and app choices, and proposed a value-centered approach to privacy that conceptually unites these relationships. In this work, we translate this theory into practice by constructing a prototype smartphone value-centered privacy assistant (VcPA) - a privacy assistant system that promotes user privacy decisions based on personal values. To do this, we designed and conducted an online survey that captured values and privacy preferences when considering whether to download an app from 273 smartphone users. Using this data, we constructed VcPA user profiles by clustering survey data based on the value rankings and stated privacy preferences. We then tested the VcPA, using selective notices, a "suggest alternatives" feature, and exploratory notices, with 77 users in a synthetic Mock App Store (MAS) setting and conducted follow-up semi-structured interviews. We establish proof-of-concept that a VcPA helps users make more value-centered app choices and identified improvements so that an assistant can be deployed on smartphone app stores.

cs.CR↗

arXiv2021

Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity

Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data. By doing so, there is a chance that the model learns properties that are unrelated to its primary task. Property Inference Attacks exploit this and aim to infer from a given model (\ie the target model) properties about the training dataset seemingly unrelated to the model's primary goal. If the training data is sensitive, such an attack could lead to privacy leakage. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack, focusing on convolutional neural network classifiers. We perform attacks on models that are trained on facial images to predict whether someone's mouth is open. Our attacks' goal is to infer whether the training dataset is balanced gender-wise. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity: for all studied architectures, the attack's accuracy is clearly over the baseline. We discuss the implication of the property inference on personal data in the light of Data Protection Regulations and Guidelines.

cs.CR↗

arXiv2020

SAFER: Development and Evaluation of an IoT Device Risk Assessment Framework in a Multinational Organization

Users of Internet of Things (IoT) devices are often unaware of their security risks and cannot sufficiently factor security considerations into their device selection. This puts networks, infrastructure and users at risk. We developed and evaluated SAFER, an IoT device risk assessment framework designed to improve users' ability to assess the security of connected devices. We deployed SAFER in a large multinational organization that permits use of private devices. To evaluate the framework, we conducted a mixed-method study with 20 employees. Our findings suggest that SAFER increases users' awareness of security issues. It provides valuable advice and impacts device selection. Based on our findings, we discuss implications for the design of device risk assessment tools, with particular regard to the relationship between risk communication and user perceptions of device complexity.

cs.CR↗