Crowd sensing worker selection mechanism and system based on blockchain position privacy protection

Abstract

The invention relates to a crowd sensing worker selection mechanism and system based on blockchain position privacy protection, and the system comprises a client and a distributed system. The client is connected with the distributed system; wherein the distributed system comprises a blockchain. The crowd sensing worker selection mechanism comprises the following steps: S11, performing user registration; S12, releasing task information; s13, the blockchain publishing information; S14, submitting work information; S15, selecting calculation by a worker; s16, signing and selecting by workers; s17, judging whether all the workers sign a contract or not; and S18, publishing a contract signing result. Sub-region optimization and global optimization technologies are sequentially used for worker coverage in a worker selection mechanism, and a more accurate calculation result is obtained; a greedy strategy is used for selecting results, and it is guaranteed that the result obtained in each stepis an optimal solution.

Description

technical field [0001] The invention relates to the field of algorithm security in information security, specifically a group intelligence sensing worker selection mechanism and system based on block chain location privacy protection. Background technique [0002] Main technology (tool) [0003] At present, there are many studies on mobile crowdsensing (MCS) worker selection (task management). If divided according to the selection area, the main methods can be divided into two categories, mainly including: (1) global worker selection mechanism ; (2) Sub-area worker selection mechanism. [0004] (1) Global worker selection mechanism [0005] The global worker selection mechanism refers to first assuming that each participant receives a fixed amount of rewards, and then selects a group of workers from all participating users, and requires all selected workers to join all perception areas and cycles of the MCS task . Existing work first investigates the research challenges ...

AI Technical Summary

Problems solved by technology

[0027] Traditional crowd sensing systems are vulnerable to Distributed Denial of Service (DDoS), remote hijacking attacks, prank attacks, etc., making services inaccessible

[0028] 2. Single point of failure

[0029] Most of the crowd sensing systems run their business on a centralized server, and the centralized server itself has a single point of failure problem

[0030] 3. Privacy disclosure

[0031] Using a centralized crowd-sensing system, since the sensitive information of participating users is stored in the database, such as user identity information, location information, etc., there is a risk of privacy leakage and data loss

[0032] 4. Task coverage objectives (data quality) cannot be guaranteed

[0033] At present, based on the centralized crowd sensing system, due to the improvement of the concept of privacy, most of the improvement solutions focus on the location privacy protection of mobile users, while ignoring the task coverage target (data quality), the location privacy protection methods used (such as spatial anonymity algorithm, etc. ) can protect the location privacy of mobile users to a certain extent, but it also increases the location uncertainty of users when completing tasks, which will have a greater impact on worker selection and data quality

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