# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 2 Literature Review
### Data Quality and Sensitivity in Crowdsourcing-Based Parking Solutions  
One of the crucial aspects of the presented phenomena is related to the quality of source data and the sensitivity and confidentiality of data related to crowdsourcing. The quality and sensitivity of source data are crucial considerations in the development and deployment of a crowdsourcing-based system for identifying available parking spaces. The effectiveness of such a system relies heavily on the accuracy, reliability, and confidentiality of the collected data. High-quality data are essential to ensure that the system provides accurate and timely information about available parking spaces. According to the literature, the main factors affecting data quality in crowdsourcing applications include data accuracy, completeness, consistency, and timeliness [30]. Data accuracy refers to the correctness of the data points, completeness pertains to the extent to which all necessary data are collected, consistency ensures that the data do not contain contradictions, and timeliness is about the data being up-to-date. To achieve high data quality, several techniques can be employed, such as:  
1. Validation and Verification: Implementing mechanisms to verify the data provided by users can significantly enhance data accuracy. For instance, combining user-reported data with data from sensors or other automated systems can help cross-verify the information [31].
2. Reputation Systems: using reputation systems where users earn trust scores based on the accuracy of their previous reports can incentivize the submission of reliable data [32].
3. Data Cleaning: regular data cleaning processes to identify and correct errors or inconsistencies in the data.  
Crowdsourcing applications often deal with sensitive and potentially confidential data, especially when they involve tracking and reporting the location of vehicles and users. Ensuring the privacy and security of these data is paramount. The General Data Protection Regulation (GDPR) and other data protection laws provide frameworks for handling personal data responsibly [33]. In Turkiye, which is the study area of this research, the Law on the Protection of Personal Data (KVKK) [34] also sets forth key principles to safeguard personal data and ensure confidentiality. Key measures to protect data sensitivity and confidentiality include:1. Anonymization and pseudonymization: techniques to anonymize or pseudonymize data can protect user identities while still allowing the system to function effectively [35].
2. Data Encryption: encrypting data both in transit and at rest ensures that unauthorized parties cannot access sensitive information [36].
3. Access Controls: implementing strict access controls to ensure that only authorized personnel can access sensitive data.
4. Short storage time of the source data, and handling processed, anonymized data, i.e., not allowing the possibility of reconstructing the source data with any sensitive elements.  
Recent studies highlight the importance of data quality and sensitivity in crowdsourcing systems. For instance, [PERSON], et al. [37] discuss the potential and pitfalls of crowdsourcing in various domains, emphasizing the need for robust data management practices. Similarly, [PERSON], et al. [38] explore methods to enhance data quality in crowdsourcing through algorithmic interventions and user incentives. Additionally, a study by [PERSON] [39] reviews privacy concerns in the context of smart cities, underlining the need for comprehensive data protection strategies.  
Ensuring high data quality and protecting data sensitivity are essential to the success of a crowdsourcing-based system for locating parking spaces. By implementing rigorous data validation, employing reputation systems, and adhering to data protection regulations, such a system can achieve its goals effectively and responsibly.