Security Challenges in IoT: Data Privacy, Trust, and Risk Mitigation

Abstract

The Internet of Things (IoT) holds transformative potential by integrating numerous connected devices into daily life and industrial systems. However, IoT deployments face fundamental security challenges, notably related to data privacy, trustworthiness, and risk mitigation. First, resource constraints in IoT devices complicate the implementation of traditional encryption, authentication, and privacy-preserving mechanisms MDPIISACA. Second, insecure defaults and vendor lock-in impair trust, as even outdated or unsupported devices remain vulnerable and unanalyzable Victorian Info Commissioner ProQuest. Third, the heterogeneity of IoT systems—diverse protocols, platforms, and ownership—thwarts unified security frameworks MDPICIO.

This paper adopts a structured methodology featuring a systematic literature review, threat modeling, stakeholder trust analysis, and risk mitigation assessment. Key findings indicate elevated privacy risks: even encrypted device traffic patterns can reveal sensitive user behavior arXiv. Default credentials and firmware flaws facilitate large-scale botnets (e.g., Mirai) and device-level compromise CIOProQuestWikipedia. These vulnerabilities jeopardize consumer trust and adoption of IoT technologies Axios.

A recommended deployment workflow spans device procurement, risk assessment, secure configuration (default credential change, encryption), trust establishment (e.g., EPID-based authentication), threat modeling, deployment, monitoring, and iterative updates WikipediaWiley Online Library. Advantages of this approach include bolstered privacy, integrity, and user trust. Disadvantages, however, arise from increased design complexity, overhead, and dependency on vendor cooperation.

In conclusion, ensuring IoT security relies on integrated strategies encompassing lightweight cryptography, vendor
accountability, interoperability, and user-centric privacy models. Future research should explore context-aware privacy
policies, IoT-tailored attestation mechanisms, and machine learning–enhanced anomaly detection to strengthen trust
and reliability in IoT ecosystems.