# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 2 Literature Review
### Related Work
AnonySense [26] is a privacy preservation mechanism through \(k\)-anonymity against the system (i.e., it is difficult to link any report to a specific user within a set of \(k\) users based on the location or timestamp) and \(l\)-anonymity against the applications (i.e., at least \(l\) reports are combined together before the aggregate is revealed to the application). The \(L\)-diversity system [27] obfuscates data to satisfy \(L\)-diversity, which means that a set of the same attribute value must have at least \(L\) sensitive information to complement \(K\)-anonymity vulnerability. AnonySense assumes that mobile nodes trust the registration authority (RA), and \(L\)-diversity also assumes the operation of a centralized system. However, internal attack against RA in AnonySense or the centralized \(L\)-diversity system could collapse the privacy preservation. Moreover, it is hard to design the reward management mechanism for the crowdsensing system in the AnonySense and \(L\)-diversity system with \(k\)- anonymity, and the data quality of both systems could go down due to the aggregation of \(l\) reports or the obfuscated data for \(L\)-diversity. Researches on privacy protection in crowdsensing environments were also performed using various technologies [28; 29; 30]. The authors in Reference [28] utilized the fog computing technology and, thus, tried to improve the efficiency of blocking privacy leakage by external attackers through fog nodes installed by service provider. Fog nodes collect sensing data and perform data aggregation However, the service provider system is still able to know the exact location of users, and the compromised fog nodes enable the privacy exposure of sensing data. The authors in Reference [29] proposed a crowdsensing system capable of protecting personal information by grouping users using blockchain technology. They tried to secure user anonymity by assigning multiple users to one blockchain node. However, due to the characteristics of the blockchain, there would be a delay in data transmission and reward payment because the transaction speed is lower than that of a general network. The authors in Reference [30] intended to provide anonymity by grouping participants in participant bidding to protect personal information exposed from bids in auction-based crowdsensing systems. Similar to the data processing nodes of our proposed mechanism, the selection of participants through group bidding is passed to the third party and, thus, bid information is protected against the service provider. However, since the third party is fixed and data collected by the service provider is still delivered to the data user, there is a vulnerability to an internal attack on this part. The data processing load is concentrated because the service provider performs data processing. Consequently, many mechanisms were proposed to prevent the exposure of personal information by external attacks. There are not many techniques considering the possibility that a service provider is malicious or compromised. In our proposed mechanism, the service provider does not play any role other than the platform provider, and an external attacker must steal a large number of data processing nodes in order to steal personal information in our proposed system.