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The digital transformation of waste management systems—encompassing Radio Frequency Identification (RFID)-equipped collection bins, Global Positioning System (GPS)-tracked vehicles, sensor-based smart containers, and pay-as-you-throw (PAYT) billing—generates continuous streams of data that carry significant privacy implications. These data intersect personal, commercial, and operational interests of municipalities, citizens, waste-collection companies, producer responsibility organisations (PROs), regulators, and researchers. Waste management presents sector-specific challenges that remain underexplored: the physical tangibility of waste enables re-identification even after digital anonymization; PAYT systems expose household behavioural patterns; and industrial waste data constitute commercially protected trade secrets. This paper analyses the anonymization requirements of all major stakeholder groups, reviews legal obligations, and proposes a layered anonymization workflow grounded in operational practice at waste-collection operators. Both classical and emerging AI-driven techniques are evaluated—including differential privacy, NLP/NER-based document redaction, OCR pipelines, computer-vision anonymization, Vision-Language Models (VLMs), agentic LLM pipelines, synthetic data generation, and federated learning. A strict requirement for all AI-driven methods is on-premise deployment. A comparative analysis against water, energy, and telecommunications utilities highlights what makes waste data unique.
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