Protecting Check-In Data Privacy in Blockchain Transactions with Preserving High Trajectory Pattern Utility

Abstract

Because the blockchain is secure and untamperable, it has been widely used in many industries, such as the financial industry, digital tokens, and e-commerce logistics. The remarkable security feature of the blockchain is that the blockchain verifies the transaction initiated on each block through the node, and its process is broadcast throughout the whole network to let everyone know. On the one hand, this ensures the security of every transaction, but on the other hand, it is easy to cause privacy disclosure problems for transaction users. Therefore, under the premise of ensuring the security of the blockchain, it has become a hot issue to protect the sensitive information of transaction users. A check-in privacy protection (CPP) algorithm based on check-in location generalization is proposed in this paper, which can be applied to blockchain transactions to solve the privacy leakage problem of transaction users’ sensitive information. CPP algorithm not only protects the privacy of check-in data but also keeps the high utility of trajectory pattern data. Firstly, location types are recommended in the sensitive check-in location generalization based on the user’s trajectory pattern by using Markov chain technology. Secondly, to make sure that the generalized locations can be scattered as much as possible to prevent the attacker from deducing back, a heuristic rule is designed to select the generalized location based on the recommended location types, and at the same time, the similarity between the anonymous trajectory and the original trajectory is maintained. In addition, a generalized location search strategy is designed to improve the efficiency of the algorithm. Based on the real spatial-temporal check-in data, the results of the experiment indicate that our algorithm can effectively protect the privacy of sensitive check-in while ensuring the high utility of trajectory pattern data.