# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 2 Related Works  
Attention to and concerns about privacy threats have been growing. Consequently, interest in researching possible solutions to protect users has also increased. In this section, we analyze studies that aim to understand privacy risks in the IoT context. Table 1 offers a summary of these research papers, with the following attributes for summarization: 'Year' indicates the publication year of the study; 'Domain' specifies the solution domain studied in the paper; and 'Cycle' denotes in which part of the data lifecycle the researched solutions are applicable, comprising Data Generation (DG), Data Storage (DS), Data Processing (DP), and Data Sharing (DSR). 'User-Centric' shows whether the approach prioritizes the user, such as allowing consent or personal control over data. 'Decentralized' assesses whether data control is distributed rather than centralized. 'GDPR' reflects whether the work aligns with the General Data Protection Regulation. Lastly, 'Data Control' evaluates the granularity of user control over their data. Below, we offer a concise summary of the chosen studies. [PERSON] et al. [14] performed a literature review of machine learning (ML) techniques for privacy protection in the IoT context. They classified the privacy threats in IoT environments into direct threats, where third parties have direct access to personal data (e.g., data leakage), and indirect threats, where third parties have access to sensible information by inference (e.g., membership inference and re-identification attacks). Their findings  
Figure 1: Paper summary. encompass ML-based solutions classified into centralized, distributed, and federated architectures that use encryption and differential privacy as privacy protection methods. [PERSON] et al. [15] analyzed the literature to understand IoT security and privacy issues, focusing on vulnerabilities, challenges, threats, and countermeasures. Based on the findings, they proposed a threat taxonomy categorizing the vulnerabilities into low, medium, and high levels and provided corresponding mitigation strategies. In their state-of-the-art literature review, [PERSON] and [PERSON] [16] explore privacy protection methods and point out the challenges to protecting personal data in an IoT environment. They aim to understand how the GDPR principles can safeguard user privacy, focusing on approaches that give users more control and address such challenges: (i) profiling, inference, and discrimination, (ii) control and context-sensitive sharing, (iii) consent and uncertainty, and (iv) honesty, trust, and transparency. In [17], the authors reviewed the role of federated learning to perform data processing while protecting user data privacy. Their review focused on research addressing privacy concerns, bandwidth limitations, and power/computing limitations. As a result, they observed that FL represents a solution to data processing, keeping the data protected. [PERSON] and [PERSON] [18] examine privacy in the IoT through a multifaceted perspective that encompasses the definition of privacy, the inherent risks of devices and edge computing architecture, potential threats, control and protection mechanisms, the handling of sensitive data, implications for users, and the evolving regulatory landscape. [PERSON] et al. [19] present a comprehensive systematic review of advancements in federated learning in the smart healthcare context. It explores key aspects, including privacy and security, through methods such as encryption, multi-party computation, and differential privacy. [PERSON] [20] presents a systematic review exploring consumer privacy in the IoT context, focusing on privacy aspects that are important to consumers. The study reveals how individual and technological factors influence consumer information disclosure behavior. These factors and relationships were synthesized into eighth propositions and provided an interactive map of the influence of each. Finally, she presents privacy protection strategies founded in the literature, summarizing their strengths and weaknesses.