The paper introduces a novel privacy-preserving semantic segmentation method that enables model training and inference using independently encrypted images for each client and image.

The paper proposes CFE-PPAR, the first compression-friendly encryption method for privacy-preserving action recognition, allowing video transformers to recognize actions directly from compressed, encr…

The paper proposes a Class-Aware Adaptive Differential Privacy (CA-ADP) framework integrated with a 3D CNN-BiLSTM architecture to significantly improve privacy-preserving fall detection performance co…

The paper proposes a decentralized, privacy-aware framework enabling smart cameras to autonomously coordinate their view coverage in public spaces while explicitly excluding sensitive regions, achievi…

The paper proposes a scalable, privacy-preserving framework for iris recognition using Fully Homomorphic Encryption (FHE), achieving accuracy comparable to cleartext while identifying the computationa…

The paper proposes a privacy-preserving system for crowd monitoring that counts individuals across different locations and time periods using face recognition without ever revealing personal identitie…

This paper provides a comprehensive, system-level comparison of MPC and FHE for Privacy-Preserving Machine Learning (PPML) across various models and environments, moving beyond single-metric latency a…

ComPrivDet is an efficient object detection method that detects privacy objects in compressed video streams by reusing inference results from I-frames, significantly reducing latency and computational…

ThermalTap presents the first passive, non-contact side-channel attack that fingerprints virtual reality (VR) applications by analyzing the long-wave infrared (LWIR) thermal radiation emitted by the h…

The paper introduces PrivHAR-Bench, a multi-tier benchmark dataset that standardizes the evaluation of the privacy-utility trade-off in video-based action recognition by applying a graduated spectrum…

The paper enhances the security of the PolyProtect biometric template protection method by proposing a key selection algorithm that significantly increases the difficulty of inverting protected face t…

The paper introduces a Privacy Policy Enforcement (PPE) framework using dual one-class density estimators to detect contextual data leakage in Retrieval-Augmented Generation (RAG) systems, achieving h…

The paper introduces 'contrastive privacy,' a formal, model-agnostic, and quantitative method for evaluating the semantic success of AI-based sanitization across multiple media modalities.

The paper introduces ImmersedPrivacy, an interactive audio-visual framework, and finds that current Vision-Language Models (VLMs) deployed in physical environments suffer from significant deficits in…

The paper proposes a multi-ciphertext privacy-preserving framework to efficiently compute high-resolution image gradients using Fully Homomorphic Encryption (FHE) by dividing the large image into smal…

This paper presents an open-source computer vision pipeline for classifying vehicle body types from naturalistic roadway video.

This paper develops optimized algorithms and a pipeline architecture for high-throughput, memory-efficient batch processing of encrypted neural network inference, significantly improving performance o…

This paper analyzes and proposes four novel attack methods—based on model parameters and model inversion—to demonstrate that existing machine unlearning techniques can inadvertently leak the categorie…

The paper proposes a lightweight post-processing framework that enhances identity continuity in thermal pedestrian tracking by leveraging scene-level spatial-temporal consistency, achieving improved t…

This paper systematically analyzes 48 studies on perception attacks against autonomous vehicles, revealing that the increasing reliance on multi-sensor fusion creates new, complex vulnerabilities that…