arXiv subjects

Bartlomiej Surma

Publications and source records attributed to Bartlomiej Surma.

3 recordsLinked to original sources

arXiv2025

Fairness and/or Privacy on Social Graphs

Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for biased or discriminatory outcomes and the vulnerability of sensitive information. This paper presents a comprehensive investigation of fairness and privacy in GNNs, exploring the impact of various fairness-preserving measures on model performance. We conduct experiments across diverse datasets and evaluate the effectiveness of different fairness interventions. Our analysis considers the trade-offs between fairness, privacy, and accuracy, providing insights into the challenges and opportunities in achieving both fair and private graph learning. The results highlight the importance of carefully selecting and combining fairness-preserving measures based on the specific characteristics of the data and the desired fairness objectives. This study contributes to a deeper understanding of the complex interplay between fairness, privacy, and accuracy in GNNs, paving the way for the development of more robust and ethical graph learning models.

cs.LG↗

arXiv2023

You Are How You Walk: Quantifying Privacy Risks in Step Count Data

Wearable devices have gained huge popularity in today's world. These devices collect large-scale health data from their users, such as heart rate and step count data, that is privacy sensitive, however it has not yet received the necessary attention in the academia. In this paper, we perform the first systematic study on quantifying privacy risks stemming from step count data. In particular, we propose two attacks including attribute inference for gender, age and education and temporal linkability. We demonstrate the severity of the privacy attacks by performing extensive evaluation on a real life dataset and derive key insights. We believe our results can serve as a step stone for deriving a privacy-preserving ecosystem for wearable devices in the future.

cs.CY↗

arXiv2019

Towards Plausible Graph Anonymization

Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs contain sensitive information, they need to be properly anonymized before release. Most of the existing graph anonymization mechanisms rely on the perturbation of the original graph's edge set. In this paper, we identify a fundamental weakness of these mechanisms: They neglect the strong structural proximity between friends in social graphs, thus add implausible fake edges for anonymization. To exploit this weakness, we first propose a metric to quantify an edge's plausibility by relying on graph embedding. Extensive experiments on three real-life social network datasets demonstrate that our plausibility metric can very effectively differentiate fake edges from original edges with AUC (area under the ROC curve) values above 0.95 in most of the cases. We then rely on a Gaussian mixture model to automatically derive the threshold on the edge plausibility values to determine whether an edge is fake, which enables us to recover to a large extent the original graph from the anonymized graph. We further demonstrate that our graph recovery attack jeopardizes the privacy guarantees provided by the considered graph anonymization mechanisms. To mitigate this vulnerability, we propose a method to generate fake yet plausible edges given the graph structure and incorporate it into the existing anonymization mechanisms. Our evaluation demonstrates that the enhanced mechanisms decrease the chances of graph recovery, reduce the success of graph de-anonymization (up to 30%), and provide even better utility than the existing anonymization mechanisms.

cs.CR↗