With the development of precise positioning technology, a growing number of location-based services (LBS) emerge. For example, visit a specific place to get the corresponding reward. This also leads to location fraud by illegal users in order to gain benefits. Thus, it is necessary to verify location certificates provided by users. However, present proof of location systems are deficient in verifying users’ location certificates while protecting users’ privacy. Users do not flexibly control their location certificates either. Based on blockchain, a kind of distributed proof of location system architecture was proposed and based on the proposed system architecture, a kind of proof of location protocol with zero-knowledge proof was proposed further. With the proposed architecture and the protocol, the proposed proof of location system allows users to freely select disclosed certificate parameters and positional accuracy according to their needs so that hierarchical location privacy protection was achieved.

　　Zero-knowledge proof of location system architecture diagram:

　　CIN: The Certificate Issuing Node, whose function is to issue location certificates for users.
　　LBSP: The Location Based Service Providers, whose function is to provide corresponding location services for legitimate users.

　　Zero-knowledge proof of location protocol flowchart:

　　Parameters of the LC:

　　Position mask:

　　The system uses Merkel Proof to allow users to disclose the certificate parameters at the field level; use location mask and zkSNARK to allow users to disclose location information with a lower accuracy level (depending on the minimum granularity of the system itself).

　　System performance:

　　The execution time of the calculation witness basically does not change with the position accuracy level, and is stable at about 650 ms; similarly, the verification time of ZK-PL does not change with the position accuracy, and is stable at about 3 800 ms. The execution time of trusted initialization increases linearly with the position accuracy level, with the minimum value of 1 024 ms and the maximum value of 4 299 ms; the execution time of generating ZK-PL increases with the position accuracy level. Because the initialization is only performed once, there is no need to perform it again during each different location service process. Therefore, the total execution time of the proposed proof of location system can be roughly estimated as the sum of the remaining three times, which is about 5 240-6 686 ms. This execution delay is acceptable for most location services, such as location-based personalized recommendation services, location-based social networks, location-based sign-in services, and so on. But for some very time-sensitive services, such as real-time positioning and navigation, the system is lacking.

　　Ratings online are often listed alongside product recommendations, but to date, limited attention has been paid as to how credible these ratings present to end-users. For lack of an effective rating verification scheme, a cheater could fabricate some high ratings and the recommended product usually does not match the corresponding ratings. To make ratings verifiable, we encapsulate each rating into a specific structure whose core is unverified rating output (URTO) that we propose. The URTO is designed based on unspent transaction output (UTXO) in Bitcoin. On this foundation, we propose the online verifiable rating system. Our system can make ratings verifiable and the evaluated validation and mining time of a rating by the whole network peers demonstrate the high reliability of our system.

　　The credible chain of online reviews:

　　System model of online verifiable ratings

　　The proposed system enables end-users to trust the authenticity of online ratings as they become verifiable. Also, we have discussed the security of the system from the perspective of cheating cost and confirmed that the system can eliminate fake ratings and retain true ones even if attacked by some cheaters. Our simulation shows that our system supports high reliability and scales well. The only drawback is that the validation time of an Rt is slightly longer due to PoW consensus algorithm. Nevertheless, this has limited impact as our scenario does not require high real-time performance. In the future, we will focus on review verification and we have provided some feasible solutions, which would be a fruitful area for our work.