Trust-based data inspection method and system in crowd sensing

Abstract

The invention belongs to the technical field of data inspection, and discloses a trust-based data inspection method and system in crowd sensing, a trust evaluation process is modeled as a social exchange process of three dimensions of historical contribution, privacy utility and data similarity, two exchange parties are a platform and a user, and the problem of cold start is solved to a certain extent; processing the user data by using localized differential privacy and quantifying the similarity of the user data, and making a balance between privacy protection and trust evaluation; according to the method, the consistency of data uploaded by a user is checked in combination with Mann-Whitney U, the checking efficiency is improved by using locality sensitive hashing (LSH), false user data is filtered, and the accuracy of a task result is improved. And finally, through theoretical analysis and experimental verification, the feasibility and effectiveness of the TDA scheme in completing the perception task are proved, and false data uploaded by malicious users in a collusion manner can be effectively filtered.

Claims

1. A trust-based data verification method for crowd intelligence sensing, characterized in that, Includes the following steps: Step 1: Model the trust assessment process as a three-dimensional social exchange model consisting of historical contributions, privacy utility, and data similarity, with the platform and users as the two parties in the exchange, in order to solve the trust initialization problem for cold-start users; Step 2: Use a localized differential privacy mechanism to perturb the user-uploaded data, quantify data similarity, and achieve a dynamic balance between privacy protection and trust assessment; Step 3: Use a user data consistency determination method based on Mann-Whitney U test, and combine it with locality-sensitive hashing algorithm to improve the test efficiency, thereby filtering out false or collusive data uploaded by malicious users; Step 4: Through theoretical derivation and experimental verification, prove that the trust-based data verification scheme is feasible and effective in completing the crowd perception task.

2. The method as described in claim 1, characterized in that, The trust assessment comprises four parts: historical contribution, privacy utility, data similarity, and trust integration, which together constitute a multi-dimensional trust measurement system.

3. The method as described in claim 2, characterized in that, The calculation of the historical contribution includes: Quantitative results of users' long-term exchange behavior are obtained by recording users' historical tasks; The user's integrity score and activity score are the main indicators. The integrity score is calculated based on the ratio of the number of times the user submits reliable data to the total number of tasks, and is used to characterize the reliability of the user in fulfilling their data quality commitments. The activity value is calculated by exponential decay based on the difference between the task completion time and the median task time, in order to measure the timeliness of the user. The reliability index and the timeliness index are weighted and summed according to preset weighting coefficients to obtain the user's historical contribution value, which is used to reflect the stability of the user's long-term behavior.

4. The method as described in claim 2, characterized in that, The privacy utility is calculated using a localized differential privacy mechanism. To meet privacy budget constraints, random noise following a Laplace distribution is introduced into the user's original data. The short-term privacy utility value is calculated based on the privacy budget of a single task, and the long-term privacy utility value is obtained by smoothing historical tasks using an exponential decay function. Ultimately, the short-term or long-term privacy utility value is selected as the privacy utility result for the current task based on whether the user has a history, in order to suppress the short-term speculative behavior of malicious users.

5. The method as described in claim 2, characterized in that, The data similarity is calculated based on group consensus. Different weights are assigned to each user based on their historical trust value, with higher-trust users receiving higher weights. Calculate the Euclidean distance between each user's data and the group consensus data, and use the weighted group standard deviation as the normalization coefficient. This weighted distance ratio is used to measure the consistency between user data and group data, thereby enabling the measurement of data authenticity.

6. The method as described in claim 2, characterized in that, The trust fusion is achieved by combining weighted fusion with nonlinear mapping. First, historical contributions, privacy benefits, and data similarity are linearly weighted according to preset weights to obtain an initial trust value. The initial trust value is then normalized using an sigmoid nonlinear mapping function to reduce the impact of extreme values ​​and capture the marginal change characteristics of trust. Output the user's final trust value to guide subsequent task allocation and data filtering.

7. The system for implementing the method as described in any one of claims 1 to 6, characterized in that, include: The trust exchange module is used to establish a multi-dimensional social exchange model between the platform and users and initialize trust parameters. The privacy and filtering module is used to perform localized differential privacy perturbations, calculate data similarity, and identify anomalous data by combining statistical testing methods. The analysis and verification module is used to calculate and verify the trust assessment results and detect malicious behavior.

8. The system as described in claim 7, characterized in that, The trust exchange module includes: The historical evaluation unit is used to calculate integrity and activity scores based on the user's historical task records. A privacy assessment unit is used to calculate short-term and long-term privacy utility based on privacy budget and noise parameters; The similarity calculation unit is used to calculate data similarity indicators based on the group consensus model.

9. The system as described in claim 7, characterized in that, The privacy and filtering module includes: Differential privacy processing unit is used to inject noise that follows a Laplace distribution into user data to achieve privacy protection; The consistency check unit is used to perform rank-based nonparametric statistical tests on user-uploaded data. The hash optimization unit is used to achieve fast matching and fake data filtering using the locality-sensitive hashing algorithm.

10. The system as described in claim 7, characterized in that, The analysis and verification module includes: The trust fusion unit is used to calculate the final trust value based on linear weighting and nonlinear mapping. The visualization evaluation unit is used to display the dynamic changes in trust among different users and to assist in system task scheduling; The malicious detection unit is used to identify and block malicious user groups who collude to upload false data.

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