Homomorphic encryption (HE) enables secure computation on encrypted data, making privacy-preserving machine learning (ML) viable for sensitive applications. Non-linear activation functions (AFs) such as ReLU, Sigmoid, and Tanh remain costly, as traditional polynomial approximations incur deep circuits and long runtimes. We propose a GPU-accelerated method that approximates these activations using fast piecewise-linear functions, formulated as a sign-decision problem to simplify computation and reduce circuit complexity. Compared to existing methods, our technique achieves a lower mean squared error (MSE) to the true activation functions, reducing MSE by approximately 55% with an error below 10(-2). Experiments demonstrate speedups of up to 128x on MNIST and 840x on CIFAR-10, with accuracy comparable to state-of-the-art methods. Across various neural architectures and datasets, our pipeline significantly reduces homomorphic evaluation time and circuit depth compared to polynomial approximations of similar accuracy.
Black-box Artificial Intelligence (AI) systems have achieved state-of-the-art accuracy in many problem domains in recent years. However, the lack of transparency of these systems is a bottleneck in their usage in high-risk applications which make automated decisions on individuals. Trustworthy AI proposes principles such as reliability, validity, privacy, fairness, and explainability among others, to mitigate risks from large-scale AI deployments in such domains. Explainable AI (XAI) is a technique of providing insights into the decision-making process of black-box systems thus enabling transparency. It plays a crucial role in communicating the rationale of automated decisions to relevant stakeholders. Though explainability is a highly desirable requirement, recent research has determined that explanations can introduce new privacy risks in AI systems. Researchers have demonstrated different types of privacy attacks on XAI deployed in production and cloud systems. Despite these risks, currently there is a lack of research into defenses for known privacy attacks in XAI. In this article, we contribute to this gap by proposing a defense mechanism for attribute inference attack on feature-based XAI. We empirically evaluate a well-known privacy preservation technique, namely, additive noise, and show its impact on privacy, explainability and utility. Our findings indicate that additive noise enables privacy while achieving faithful explanations and without compromising model utility.
User profiling based on browsing behavior has traditionally been applied to improve web personalization and marketing strategies. However, leveraging browsing patterns to assess cybersecurity risks remains underexplored. In this paper, we propose a profiling framework based on domain name system (DNS) traffic analysis. Our approach models user browsing behavior using two main factors: browsing intent and domain reputation. By aggregating risk weights derived from accessed domains, we compute a personalized browsing risk score that reflects the user’s exposure to online threats. We validate the effectiveness of our framework through experiments that demonstrate its ability to differentiate users with varying levels of browsing risk. Our findings offer new insights into user-centric cybersecurity assessment using minimal yet meaningful data sources.
In an era of increasing digital privacy risks, images shared online can inadvertently reveal sensitive location data through identifiable elements such as logos, road signs, and text. These disclosures enable unauthorized tracking and data mining, raising serious privacy concerns. This paper proposes a novel framework that combines object detection and generative inpainting for privacy preserving image reconstruction. A Mask R-CNN model is trained on three diverse datasets to detect and segment location identifiable elements accurately. The detected regions are then de-identified using a proposed Denoising Diffusion Probabilistic Model (DDPM)-based inpainting method, which preserves scene integrity by ensuring geometric consistency and natural lighting. Unlike traditional inpainting methods, the proposed framework dynamically refines image reconstructions through controlled denoising, achieving high realism. The effectiveness of the method is evaluated using standard image quality metrics, including PSNR, SSIM, and FID, alongside subjective visual assessments. Experimental results show that the proposed approach outperforms baseline models such as CNNs and GANs, offering a robust solution for privacy preserving image reconstruction.
Online review platforms have drastically reshaped how we interact, make purchasing decisions, and engage with digital content. However, the rise of deceptive content, privacy breaches, and misinformation has undermined online content credibility, impacting the trustworthiness of the information shared. To address these issues, we propose a robust framework for automated trust assessment of online reviews, focusing on identifying deceptive online content. The core of our approach is the trust vector, a novel feature representation that captures key user engagement factors influencing content trustworthiness. By applying the Weighted Trust Scoring Method (WTSM), we calculate a weighted trust score that strengthens the model's interpretability and effectiveness in trust evaluation. The proposed model is evaluated on two benchmark datasets-the Deceptive Opinion Corpus Dataset and the Yelp Review Dataset. The framework achieves classification accuracies of 85% and 87%, respectively, demonstrating its effectiveness in distinguishing deceptive from truthful content.
