Abstract
Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.
Highlights
With the rapid development of the mobile Internet and information technology, location-based services (LBSs) are widely used and have become an indispensable part of people’s lives
This paper proposes a blockchain-based multiple-attribute decision making (MADM) approach for location privacy protection, and the research is primarily performed in the following areas: 1) We introduce the concept of credit value, propose the MADM algorithm to convert credit data from users’ lives into credit values, and propose a credit value reward and punishment mechanism to limit the adverse behaviors of the requestors and participants
The proposed MADM algorithm converts credit data from users’ real lives into credit values as an important constraint for users in constructing anonymous zones; a reward and punishment mechanism is used to constrain the bad behaviors of users in constructing the anonymous zone and to motivate them to actively participate in the construction of the anonymous zone to minimize the probability of user location leakage and protect location privacy
Summary
With the rapid development of the mobile Internet and information technology, location-based services (LBSs) are widely used and have become an indispensable part of people’s lives. Yang et al [19] noted that the original distributed K-anonymity privacy protection schemes assume that participants are honest and reliable To address this problem, an auction incentive mechanism is proposed that allows multiple requesting users to obtain the true locations of the collaborating users through an auction, incentivizing users to participate in the construction of an anonymous zone. The proposed MADM algorithm converts credit data from users’ real lives into credit values as an important constraint for users in constructing anonymous zones; a reward and punishment mechanism is used to constrain the bad behaviors of users in constructing the anonymous zone and to motivate them to actively participate in the construction of the anonymous zone to minimize the probability of user location leakage and protect location privacy
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