# Toward Youth-Centered Privacy-by-Design in Smart Devices: A Systematic Review

arXiv: https://arxiv.org/abs/2601.11598 · HTML: https://mlsanity.com/paper/2601.11598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 126 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications](https://mlsanity.com/paper/1811.12476/) (2018)
- [Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review](https://mlsanity.com/paper/2412.16369/) (2024)
- [Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI](https://mlsanity.com/paper/2503.11950/) (2025)
- [Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics](https://mlsanity.com/paper/1512.06000/) (2015)
- [RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems](https://mlsanity.com/paper/2208.13032/) (2022)
- [Covert Surveillance in Smart Devices: A SCOUR Framework Analysis of Youth Privacy Implications](https://mlsanity.com/paper/2510.24072/) (2025)
- [Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives](https://mlsanity.com/paper/2501.13320/) (2025)
- [Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance](https://mlsanity.com/paper/2503.11947/) (2025)
- [Privacy of the Internet of Things: A Systematic Literature Review (Extended Discussion)](https://mlsanity.com/paper/1611.03340/) (2016)
- [Towards Resilience and Autonomy-based Approaches for Adolescents Online Safety](https://mlsanity.com/paper/2504.15533/) (2025)

## Abstract (arXiv)

This literature review evaluates privacy-by-design frameworks, tools, and policies intended to protect youth in AI-enabled smart devices using a PRISMA-guided workflow. Sources from major academic and grey-literature repositories from the past decade were screened. The search identified 2,216 records; after deduplication and screening, 645 articles underwent eligibility assessment, and 122 were included for analysis. The corpus was organized along three thematic categories: technical solutions, policy/regulatory measures, and education/awareness strategies. Findings reveal that while technical interventions such as on-device processing, federated learning, and lightweight encryption significantly reduce data exposure, their adoption remains limited. Policy frameworks, including the EU's GDPR, the UK Age-Appropriate Design Code, and Canada's PIPEDA, provide important baselines but are hindered by gaps in enforcement and age-appropriate design obligations, while educational initiatives are rarely integrated systematically into curricula. Overall, the corpus skews toward technical solutions (67%) relative to policy (21%) and education (12%), indicating an implementation gap outside the technical domain. To address these challenges, we recommend a multi-stakeholder model in which policymakers, manufacturers, and educators co-develop inclusive, transparent, and context-sensitive privacy ecosystems. This work advances discourse on youth data protection by offering empirically grounded insights and actionable recommendations for the design of ethical, privacy-preserving AI systems tailored to young users.