# Privacy Framework for the Development of IoT-Based Systems
## 6 Proposal of Privacy Guidelines
### Survey Results
#### 6.2.1 Analysis Part 1: Categorization of Guidelines Based on Importance and User Feedback  
To conduct this analysis, each guideline was evaluated based on its _relevance_, _practically_, and _clarity_, using a five-point Likert scale. This structured evaluation enabled a comprehensive assessment of the guidelines, grounded in participant feedback. Based on the analysis, the guidelines were categorized into three distinct groups:  
_(a) High-Importance Guidelines_  
These guidelines consistently received high ratings across all three dimensions, with a mean score of 4.0 and above, signifying their critical role in safeguarding IoTS privacy.  
* Encrypted Data Communication (4.51)
* Encrypted Data Storage (4.44)
* Data Anonymization (4.42)
* Minimize Data Storage (4.23)
* Information Disclosure (4.40)
* Compliance with Regulations (4.35)  
Figure 7: Participants’ familiarity with privacy-focused design in IoT.  
\(\bullet\)\(\quad\)Logging (4.30)  
\(\bullet\)\(\quad\)Auditing (4.30)  
\(\bullet\)\(\quad\)Minimize Raw Data Intake (4.0)  
These guidelines focus on fundamental privacy practices, including data encryption, user awareness, and regulatory compliance, which are essential for ensuring robust security in IoTSs.  
_(b)\(\quad\)Medium-Importance Guidelines_  
These guidelines, with scores between 3.7 and 3.99, were considered valuable but may require refinement or be context-dependent, affecting their applicability across different IoT scenarios.  
\(\bullet\)\(\quad\)Minimize Data Acquisition (3.86)  
\(\bullet\)\(\quad\)Minimize Number of Data Sources (3.79)  
\(\bullet\)\(\quad\)Reduce Data Granularity (3.88)  
\(\bullet\)\(\quad\)Knowledge Discovery-Based Aggregation (3.91)  
\(\bullet\)\(\quad\)Distributed Data Storage (3.88)  
\(\bullet\)\(\quad\)Category-Based Aggregation (3.79)  
\(\bullet\)\(\quad\)Query Answering Without Raw Data (3.95)  
These guidelines focus on data efficiency and control mechanisms; however, their feasibility often depends on the specific nature of IoTSs, as some systems require extensive data collection to operate effectively.  
_(c)\(\quad\)Low-Importance Guidelines_  
These guidelines received scores below 3.7, indicating limited practical implementation or a lack of clarity.  
\(\bullet\)\(\quad\)Minimize Knowledge Discovery (3.16)  
\(\bullet\)\(\quad\)Open-Source Policy for Transparency (3.37)  
\(\bullet\)\(\quad\)Hidden Data Routing (3.49)  
\(\bullet\)\(\quad\)Chain Aggregation (3.21)  
\(\bullet\)\(\quad\)Minimize Data Retention Period (3.5)  
\(\bullet\)\(\quad\)Repeated Query Blocking (3.0)  
\(\bullet\)\(\quad\)Distributed Data Processing (3.0)  
\(\bullet\)\(\quad\)Geography-Based Aggregation (3.5)  
\(\bullet\)\(\quad\)Time-Period Based Aggregation (3.0)  
\(\bullet\)\(\quad\)Control (3.5)  
\(\bullet\)\(\quad\)Data Flow Diagrams (3.5)  
\(\bullet\)\(\quad\)Certification (3.0)  
\(\bullet\)\(\quad\)Standardization (3.0)  
\(\bullet\)\(\quad\)Encrypted Data Processing (3.5)  
These guidelines may be perceived as too restrictive, too complex to implement, or insufficiently defined. The lower ratings suggest the need for revisions to improve their clarity and practical utility, with potential adjustments to make them more actionable.