# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 2 Literature Review
### Related Work
If such preprocessing (e.g., removing the license plate number) is not performed by the sensing data provider, there is a risk of privacy exposure (e.g., by identifying the license plate number and revealing the location of the user). However, services that do not require extensive data processing (e.g., environmental data, such as temperature or vehicle CO\({}_{2}\) emissions) [22] are less vulnerable to privacy exposure. Moreover, crowdsensing services involving spatiotemporal data (e.g., vehicle trajectory) are vulnerable in terms of location or trace privacy [22]. Closed crowdsensing services irrelevant to spatiotemporal characteristics, such as industrial management [23], are resistant to privacy exposure. In conclusion, distributed, overall, pull, participatory, non-extensive-preprocessing, and non-spatiotemporal crowdsensing services are advantageous compared to centralized, partial, push, periodic, extensive-preprocessing, and spatiotemporal services in terms of privacy protection. It is more difficult for attackers to access sensitive personal information in distributed crowdsensing services than in centralized because this information is processed by multiple users. The overall crowdsensing service sends requests to all users instead of checking user location, status, and so on; thus, it does not experience personal information exposure problems caused by user monitoring. The pull crowdsensing service does not experience personal information exposure problems caused by continuous communication because users check requests directly, and no continuous communication with the service provider is required. In the participatory crowdsensing service, users provide sensing data voluntarily. Thus, if personal information exposure occurs, users can respond immediately by, for example, removing this information or discontinuing participation. We consider these architecture and service types in the design of a crowdsensing system that is resistant to privacy exposure. The authors in Reference [19] proposed a participatory crowdsensing system using smartphones. The service provider transmits a request to all user applications, and users may respond to the request of their choice. A user can reduce the risk of personal information exposure by providing only selected sensing data to the service provider. However, the central service provider handles all stages of data collection, including request allocation and data processing, so that the personal information of a specific user can be inferred from the data provided by multiple users. In this paper, we propose a system for reducing the risk of personal information exposure in participatory crowdsensing by applying distributed crowdsensing in which users participate in data processing. The authors in Reference [25] proposed a partial crowdsensing system for road condition monitoring. The application continuously transmits user location information to the service provider. To obtain road condition information at a certain location, the service provider sends a request only to the user at that location; thus, this method is highly efficient. However, as the \(SP\) continuously collects the location information of all users, the risk of personal information exposure is high. In the present study, to reduce this in a partial crowdsensing system, we use a pull method whereby requests are not allocated to users by the service provider, but rather a user application selects whether to display a request. We propose a distributed crowdsensing system of pull, partial, and participation type that exhibits relatively low personal information exposure risk, and we implement it using the Kaa IoT platform, CoAP light messaging protocol, and MongoDB. On the other hand, the proposed mechanism separates the sensing data processing from the reward payment processing in order to preserve the privacy in the data processing by the service provider in the crowdsensing system. Basically, mechanisms to prevent privacy leakage were studied [26; 27].