# Privacy Framework for the Development of IoT-Based Systems
## 6 Proposal of Privacy Guidelines
### Survey Design and Implementation  
The primary objective of our survey is to evaluate 30 privacy-oriented guidelines, originally proposed by [PERSON] et al. [22], which are designed to guide the development of secure and privacy-conscious IoTS solutions. The survey focuses on gathering participants' insights regarding the following aspects of each guideline:  
* Clarity: The ease of understanding the guideline. * Practicality: The feasibility of implementing the guideline. * Relevance: The significance of the guideline in ensuring privacy in IoTSs. Participants were also encouraged to offer suggestions for improvement to enhance the utility of these guidelines for developers working in the IoT field. Each of the above criteria was rated on a 5-point Likert scale, ranging from 1 (Low) to 5 (High). By collecting this feedback, the study aims to refine the guidelines, making them both actionable and aligned with the practical needs of IoTS solution development. To ensure a diverse range of perspectives, the survey was distributed to a varied group of 75 participants, including developers, industry experts, academics, and students. The survey was conducted using SurveyMonkey, a widely recognized online data collection tool, which ensured ease of access for all participants. The selection of participants was intentional, aimed at capturing a wide range of experiences and expertise directly relevant to IoTS privacy. The survey was structured into three sections:1. **Demographic Information:** Collected data on participants' backgrounds and assessed their general understanding of IoTS privacy. 2. **Guudeline Evaluation:** Asked participants to evaluate the 30 privacy guidelines based on their clarity, practicality, and relevance. 3. **Feedback and Suggestions:** Provided space for participants to offer additional comments, suggestions, and potential improvements to further refine the guidelines. Figure 4 presents a segment of the demographic information of the participants, focusing specifically on their professional roles. The figure categorizes participants based on their roles, including software developers, IoT engineers, data scientists, and security specialists, as well as other specialized positions specified by the respondents themselves, such as cybersecurity researchers, Quality Assurance (QA) professionals, systems engineers, PhD students specializing in privacy or cybersecurity research, and professors working in areas like software engineering and software development. Although Figure 4 shows 0% for security specialists, this is because some relevant roles have been categorized under \"Other\" due to mismatches in exact job titles. This demographic breakdown highlights the diversity of the participant sample and emphasizes the relevance of their insights to the study, ensuring that the findings are informed by a wide range of perspectives from both academic and industry professionals. Figure 5 illustrates the years of experience of the participants, providing valuable insight into their level of expertise and supporting the overall reliability of their responses. The data reveals that a significant portion of participants possesses extensive experience in their respective fields, which enhances the credibility of their feedback. The distribution of experience is categorized into four groups: 0-2 years, 3-5 years, 6-10 years, and over 10 years. The chart shows that a considerable proportion of participants have more than 10 years of experience, indicating that the sample includes a substantial number of seasoned experts. Additionally, a notable percentage of participants fall within the 6-10 years range, further reinforcing the depth of professional experience within the group. In contrast, the percentage of participants with 0-2 years or 3-5 years of experience is comparatively lower, suggesting that the number of less-experienced participants is minimal.