arXiv subjects

M. Emre Gursoy

Publications and source records attributed to M. Emre Gursoy.

5 recordsLinked to original sources

arXiv2026

Fast Simulation Algorithms for OLH using Binomial Modeling

Optimized Local Hashing (OLH) is a widely used hash-based Local Differential Privacy (LDP) protocol, and simulation-based experimentation is the standard approach for evaluating OLH and OLH-based applications in research. However, the existing OLH simulations have $O(nd)$ computational complexity, where $n$ is the user population size and $d$ is the domain size, and can lead to significant execution times as $n$ and $d$ grow. In this paper, we propose two fast simulation algorithms for OLH (2-Binom and 3-Binom) grounded in Binomial modeling. Our key insight is that, for any domain value $v$, the total number of users whose perturbed reports support $v$ can be decomposed into a sum of two or three Binomial random variables. Using this insight, our algorithms reduce the simulation complexity to $O(n + d)$ without hurting statistical equivalence. In particular, we theoretically prove that both algorithms yield unbiased frequency estimations with variances identical to those of the original OLH simulations. Experiments on real-world datasets confirm that both approaches reduce execution times from several minutes to milliseconds, yielding significant speedups with no change in utility.

cs.CR↗

arXiv2025

Strategic Sample Selection for Improved Clean-Label Backdoor Attacks in Text Classification

Backdoor attacks pose a significant threat to the integrity of text classification models used in natural language processing. While several dirty-label attacks that achieve high attack success rates (ASR) have been proposed, clean-label attacks are inherently more difficult. In this paper, we propose three sample selection strategies to improve attack effectiveness in clean-label scenarios: Minimum, Above50, and Below50. Our strategies identify those samples which the model predicts incorrectly or with low confidence, and by injecting backdoor triggers into such samples, we aim to induce a stronger association between the trigger patterns and the attacker-desired target label. We apply our methods to clean-label variants of four canonical backdoor attacks (InsertSent, WordInj, StyleBkd, SynBkd) and evaluate them on three datasets (IMDB, SST2, HateSpeech) and four model types (LSTM, BERT, DistilBERT, RoBERTa). Results show that the proposed strategies, particularly the Minimum strategy, significantly improve the ASR over random sample selection with little or no degradation in the model's clean accuracy. Furthermore, clean-label attacks enhanced by our strategies outperform BITE, a state of the art clean-label attack method, in many configurations.

cs.CR↗

arXiv2025

Win-k: Improved Membership Inference Attacks on Small Language Models

Small language models (SLMs) are increasingly valued for their efficiency and deployability in resource-constrained environments, making them useful for on-device, privacy-sensitive, and edge computing applications. On the other hand, membership inference attacks (MIAs), which aim to determine whether a given sample was used in a model's training, are an important threat with serious privacy and intellectual property implications. In this paper, we study MIAs on SLMs. Although MIAs were shown to be effective on large language models (LLMs), they are relatively less studied on emerging SLMs, and furthermore, their effectiveness decreases as models get smaller. Motivated by this finding, we propose a new MIA called win-k, which builds on top of a state-of-the-art attack (min-k). We experimentally evaluate win-k by comparing it with five existing MIAs using three datasets and eight SLMs. Results show that win-k outperforms existing MIAs in terms of AUROC, TPR @ 1% FPR, and FPR @ 99% TPR metrics, especially on smaller models.

cs.AI↗

arXiv2025

Defending Against Beta Poisoning Attacks in Machine Learning Models

Poisoning attacks, in which an attacker adversarially manipulates the training dataset of a machine learning (ML) model, pose a significant threat to ML security. Beta Poisoning is a recently proposed poisoning attack that disrupts model accuracy by making the training dataset linearly nonseparable. In this paper, we propose four defense strategies against Beta Poisoning attacks: kNN Proximity-Based Defense (KPB), Neighborhood Class Comparison (NCC), Clustering-Based Defense (CBD), and Mean Distance Threshold (MDT). The defenses are based on our observations regarding the characteristics of poisoning samples generated by Beta Poisoning, e.g., poisoning samples have close proximity to one another, and they are centered near the mean of the target class. Experimental evaluations using MNIST and CIFAR-10 datasets demonstrate that KPB and MDT can achieve perfect accuracy and F1 scores, while CBD and NCC also provide strong defensive capabilities. Furthermore, by analyzing performance across varying parameters, we offer practical insights regarding defenses' behaviors under varying conditions.

cs.CR↗

arXiv2024

Injecting Bias into Text Classification Models using Backdoor Attacks

The rapid growth of natural language processing (NLP) and pre-trained language models have enabled accurate text classification in a variety of settings. However, text classification models are susceptible to backdoor attacks, where an attacker embeds a trigger into the victim model to make the model predict attacker-desired labels in targeted scenarios. In this paper, we propose to utilize backdoor attacks for a new purpose: bias injection. We develop a backdoor attack in which a subset of the training dataset is poisoned to associate strong male actors with negative sentiment. We execute our attack on two popular text classification datasets (IMDb and SST) and seven different models ranging from traditional Doc2Vec-based models to LSTM networks and modern transformer-based BERT and RoBERTa models. Our results show that the reduction in backdoored models' benign classification accuracy is limited, implying that our attacks remain stealthy, whereas the models successfully learn to associate strong male actors with negative sentiment (100% attack success rate with >= 3% poison rate). Attacks on BERT and RoBERTa are particularly more stealthy and effective, demonstrating an increased risk of using modern and larger models. We also measure the generalizability of our bias injection by proposing two metrics: (i) U-BBSR which uses previously unseen words when measuring attack success, and (ii) P-BBSR which measures attack success using paraphrased test samples. U-BBSR and P-BBSR results show that the bias injected by our attack can go beyond memorizing a trigger phrase.

cs.CR↗