arXiv subjects

Niki Pissinou

Publications and source records attributed to Niki Pissinou.

3 recordsLinked to original sources

arXiv2025

Victim-Centred Abuse Investigations and Defenses for Social Media Platforms

Online abuse, a persistent aspect of social platform interactions, impacts user well-being and exposes flaws in platform designs that include insufficient detection efforts and inadequate victim protection measures. Ensuring safety in platform interactions requires the integration of victim perspectives in the design of abuse detection and response systems. In this paper, we conduct surveys (n = 230) and semi-structured interviews (n = 15) with students at a minority-serving institution in the US, to explore their experiences with abuse on a variety of social platforms, their defense strategies, and their recommendations for social platforms to improve abuse responses. We build on study findings to propose design requirements for abuse defense systems and discuss the role of privacy, anonymity, and abuse attribution requirements in their implementation. We introduce ARI, a blueprint for a unified, transparent, and personalized abuse response system for social platforms that sustainably detects abuse by leveraging the expertise of platform users, incentivized with proceeds obtained from abusers.

cs.CR↗

arXiv2021

The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems

Adversarial examples, or nearly indistinguishable inputs created by an attacker, significantly reduce machine learning accuracy. Theoretical evidence has shown that the high intrinsic dimensionality of datasets facilitates an adversary's ability to develop effective adversarial examples in classification models. Adjacently, the presentation of data to a learning model impacts its performance. For example, we have seen this through dimensionality reduction techniques used to aid with the generalization of features in machine learning applications. Thus, data transformation techniques go hand-in-hand with state-of-the-art learning models in decision-making applications such as intelligent medical or military systems. With this work, we explore how data transformations techniques such as feature selection, dimensionality reduction, or trend extraction techniques may impact an adversary's ability to create effective adversarial samples on a recurrent neural network. Specifically, we analyze it from the perspective of the data manifold and the presentation of its intrinsic features. Our evaluation empirically shows that feature selection and trend extraction techniques may increase the RNN's vulnerability. A data transformation technique reduces the vulnerability to adversarial examples only if it approximates the dataset's intrinsic dimension, minimizes codimension, and maintains higher manifold coverage.

cs.LG↗

arXiv2019

$n$-VDD: Location Privacy Protection Based on Voronoi-Delaunay Duality

To date, location privacy protection is a critical issue in Location-Based Services (LBS). In this work, we propose a novel geometric framework based on the classical discrete geometric structure, the Voronoi-Delaunay duality (VDD). We utilize the fact that the user location cannot be recovered if only given an irregular $n$-sided Voronoi cell around it, and the anonymity zone is the intersection of all the parallel strips perpendicular to and bounded by $n$ Voronoi edges. The irregular Voronoi cell and its variations can be used as the concealing space to hide the user location or the region of interest and submitted to the LBS server. Within this framework, we propose multiple typical anonymizing models by introducing irregularity to the convex regular VDD structure by shifting the interior Voronoi cell, exterior Delaunay polygon, sector rays, or their combinations. The proposed methods are efficient by taking advantage of the VDD principle where main computations are linear line-line intersections. Experiments with various parameters demonstrate the efficiency and efficacy of the proposed $n$-VDD framework.

cs.CG↗