Privacy Preservation and Identity Tracing Prevention in AI-Driven Eye Tracking for Interactive Learning Environments

• URL: http://arxiv.org/abs/2509.05376v1
• Date: Thu, 04 Sep 2025 13:08:06 GMT
• Title: Privacy Preservation and Identity Tracing Prevention in AI-Driven Eye Tracking for Interactive Learning Environments
• Authors: Abdul Rehman, Are Dæhlen, Ilona Heldal, Jerry Chun-wei Lin,
• Abstract summary: Eye-tracking technology can aid in understanding neurodevelopmental disorders and tracing a person's identity.<n>This paper proposes a human-centered framework designed to prevent identity backtracking while preserving the pedagogical benefits of AI-powered eye tracking.
• Score: 17.850299218352102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Eye-tracking technology can aid in understanding neurodevelopmental disorders and tracing a person's identity. However, this technology poses a significant risk to privacy, as it captures sensitive information about individuals and increases the likelihood that data can be traced back to them. This paper proposes a human-centered framework designed to prevent identity backtracking while preserving the pedagogical benefits of AI-powered eye tracking in interactive learning environments. We explore how real-time data anonymization, ethical design principles, and regulatory compliance (such as GDPR) can be integrated to build trust and transparency. We first demonstrate the potential for backtracking student IDs and diagnoses in various scenarios using serious game-based eye-tracking data. We then provide a two-stage privacy-preserving framework that prevents participants from being tracked while still enabling diagnostic classification. The first phase covers four scenarios: I) Predicting disorder diagnoses based on different game levels. II) Predicting student IDs based on different game levels. III) Predicting student IDs based on randomized data. IV) Utilizing K-Means for out-of-sample data. In the second phase, we present a two-stage framework that preserves privacy. We also employ Federated Learning (FL) across multiple clients, incorporating a secure identity management system with dummy IDs and administrator-only access controls. In the first phase, the proposed framework achieved 99.3% accuracy for scenario 1, 63% accuracy for scenario 2, and 99.7% accuracy for scenario 3, successfully identifying and assigning a new student ID in scenario 4. In phase 2, we effectively prevented backtracking and established a secure identity management system with dummy IDs and administrator-only access controls, achieving an overall accuracy of 99.40%.

Related papers

• Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education [53.25191965774678]
  We propose a framework exploiting cross-device federated learning to address different manifestations of behavioral and cognitive disengagement during remote learning.<n>We fit video-based cognitive disengagement detection models using facial expressions and gaze features.<n>Our results show great promise for privacy-preserving educational technologies promoting learner engagement.
  arXiv  Detail & Related papers  (2026-02-10T15:40:01Z)
• Face Identity Unlearning for Retrieval via Embedding Dispersion [0.0]
  Face recognition systems rely on learning highly discriminative and compact identity clusters to enable accurate retrieval.<n>In this work, we study the problem of face identity unlearning for retrieval systems and present its inherent challenges.<n>The primary challenge is to achieve this forgetting effect while preserving the discriminative structure of the embedding space.
  arXiv  Detail & Related papers  (2025-12-15T13:35:28Z)
• What's the next frontier for Data-centric AI? Data Savvy Agents [71.76058707995398]
  We argue that data-savvy capabilities should be a top priority in the design of agentic systems.<n>We propose four key capabilities to realize this vision: Proactive data acquisition, Sophisticated data processing, Interactive test data synthesis, and Continual adaptation.
  arXiv  Detail & Related papers  (2025-11-02T17:09:29Z)
• Emergent AI Surveillance: Overlearned Person Re-Identification and Its Mitigation in Law Enforcement Context [2.3124669700253553]
  Generic instance search models can dramatically reduce the manual effort required to analyze vast surveillance footage during criminal investigations by retrieving specific objects of interest to law enforcement.<n>However, our research reveals an unintended emergent capability: through overlearning, these models can single out specific individuals even when trained on datasets without human subjects.<n>This capability raises concerns regarding identification and profiling of individuals based on their personal data.
  arXiv  Detail & Related papers  (2025-10-07T15:23:16Z)
• Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence [0.0]
  CRID is a cross-modal framework combining Large Vision-Language Models, Graph Attention Networks, and representation learning.<n>Our approach focuses on identifying and leveraging interpretable features, enabling the detection of semantically meaningful PII beyond low-level appearance cues.<n>Our experiments show improved performance in practical cross-dataset Re-ID scenarios.
  arXiv  Detail & Related papers  (2025-07-02T09:10:33Z)
• Differential Privacy in Machine Learning: From Symbolic AI to LLMs [49.1574468325115]
  Differential privacy provides a formal framework to mitigate privacy risks.<n>It ensures that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm.
  arXiv  Detail & Related papers  (2025-06-13T11:30:35Z)
• Towards Scalable Defenses against Intimate Partner Infiltrations [9.694038607827169]
  In Intimate Partner Infiltration (IPI) perpetrators leverage close proximity and personal knowledge to circumvent standard protections.<n>We present AID, an Automated IPI Detection system that continuously monitors for unauthorized access and suspicious behaviors on smartphones.<n>AID achieves highly accurate detection of non-owner access and fine-grained IPI-related activities, attaining a false positive rate of 1.6%, which is 11x lower than existing methods, and an end-to-end F1 score of 0.981.
  arXiv  Detail & Related papers  (2025-02-06T00:07:08Z)
• Task-Agnostic Federated Learning [4.041327615026293]
  This study addresses task-agnostic and generalization problem on un-seen tasks by adapting self-supervised FL framework.<n> utilizing Vision Transformer (ViT) as consensus feature encoder for self-supervised pre-training, no initial labels required, the framework enabling effective representation learning across diverse datasets and tasks.
  arXiv  Detail & Related papers  (2024-06-25T02:53:37Z)
• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification [36.91680117072686]
  We propose Erasing, Transforming, and Noising Defense Network (ETNDNet) to solve occluded person re-ID. In the proposed ETNDNet, we randomly erase the feature map to create an adversarial representation with incomplete information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians.
  arXiv  Detail & Related papers  (2023-07-14T06:42:21Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture [2.9392867898439006]
  We propose a two-level deep learning architecture for affect recognition. The architecture consists of recurrent neural networks to capture the temporal relationships amongst the features.
  arXiv  Detail & Related papers  (2021-11-14T13:52:57Z)
• Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation [100.69458267888962]
  Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. Due to legal and privacy issues, training data (real face images and spoof images) are not allowed to be directly shared between different data sources. We propose a Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation framework.
  arXiv  Detail & Related papers  (2021-10-25T02:51:05Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.