arXiv subjects

Anass Sedrati

Publications and source records attributed to Anass Sedrati.

4 recordsLinked to original sources

arXiv2026

Decentralization and Governance in IoT: Bitcoin and Wikipedia Case

In the era of digital revolution many contemporary events that changed the world were shaped through the internet. Nowadays, the emergence of internet of things (IoT), combining physical objects with virtual networks is expected to have even more influence. This new 'decentralised' structure in the world raises questions such as power, governance and the notion of democracy online. The aim of this paper is to investigate these notions. We have taken the examples of Bitcoin and Wikipedia and examined their decision-making process. Our analysis has found some inconsistencies in their policies, that are in contradiction with democracy and consensus principles of governance. Starting from our findings, we present further improvements that can be used to achieve more democracy and equity in the digital context.

cs.CY↗

arXiv2026

Unconsented Sensing: A Sociotechnical Governance Framework for 6G ISAC

The forthcoming deployment of 6G Integrated Sensing and Communication (ISAC) will transform cellular infrastructure into pervasive, continuous environmental and biometric sensing grids. While current telecom standardization efforts (e.g., 3GPP, ETSI) have formally recognized privacy and trustworthiness as critical pillars for 6G, their proposed mitigations remain overwhelmingly technocentric, relying on cryptographic anonymization and physical layer security. This approach critically underestimates the sociotechnical and legal complexities of the downstream machine learning (ML) models required to interpret raw sensing data, creating a profound collision with existing digital rights legislation. This position paper argues that technical security is insufficient. ISAC trustworthiness must be redefined as mandatory regulatory and sociotechnical compliance. We identify the specific legal friction points between continuous ISAC surveillance and the mandates of emerging global digital rights regimes, using the stringent requirements of the EU AI Act and GDPR as our primary regulatory baselines. To bridge this gap, we propose a governance framework centered on three pillars: Purpose-bound sensing activation, citizen transparency mechanisms, and algorithmic accountability for ISAC-driven ML models. Ultimately, this paper provides a regulatory roadmap to prevent the illegal deployment of 6G sensing infrastructures and ensure they remain viable before physical deployment.

cs.NI↗

arXiv2026

L-ReLF: A Framework for Lexical Dataset Creation

This paper introduces the L-ReLF (Low-Resource Lexical Framework), a novel, reproducible methodology for creating high-quality, structured lexical datasets for underserved languages. The lack of standardized terminology, exemplified by Moroccan Darija, poses a critical barrier to knowledge equity in platforms like Wikipedia, often forcing editors to rely on inconsistent, ad-hoc methods to create new words in their language. Our research details the technical pipeline developed to overcome these challenges. We systematically address the difficulties of working with low-resource data, including source identification, utilizing Optical Character Recognition (OCR) despite its bias towards Modern Standard Arabic, and rigorous post-processing to correct errors and standardize the data model. The resulting structured dataset is fully compatible with Wikidata Lexemes, serving as a vital technical resource. The L-ReLF methodology is designed for generalizability, offering other language communities a clear path to build foundational lexical data for downstream NLP applications, such as Machine Translation and morphological analysis.

cs.CL↗

arXiv2025

Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning

The right to privacy, enshrined in various human rights declarations, faces new challenges in the age of artificial intelligence (AI). This paper explores the concept of the Right to be Forgotten (RTBF) within AI systems, contrasting it with traditional data erasure methods. We introduce Forgotten by Design, a proactive approach to privacy preservation that integrates instance-specific obfuscation techniques during the AI model training process. Unlike machine unlearning, which modifies models post-training, our method prevents sensitive data from being embedded in the first place. Using the LIRA membership inference attack, we identify vulnerable data points and propose defenses that combine additive gradient noise and weighting schemes. Our experiments on the CIFAR-10 dataset demonstrate that our techniques reduce privacy risks by at least an order of magnitude while maintaining model accuracy (at 95% significance). Additionally, we present visualization methods for the privacy-utility trade-off, providing a clear framework for balancing privacy risk and model accuracy. This work contributes to the development of privacy-preserving AI systems that align with human cognitive processes of motivated forgetting, offering a robust framework for safeguarding sensitive information and ensuring compliance with privacy regulations.

cs.LG↗