Task Allocation Under Geo-Indistinguishability via Group-Based Noise Addition

Zhang, Pengfei; Cheng, Xiang; Su, Sen; Wang, Ning

doi:10.1109/tbdata.2022.3215467

Cited by 10 publications

(1 citation statement)

References 54 publications

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“…Geo-I for task allocation in spatial crowdsourcing has been investigated [12]. An optimized global grouping with the adaptive local adjustment method OGAL with a convergence guarantee was proposed and proven that it works.…”

Section: Overview Of Location Privacy Researchmentioning

confidence: 99%

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Isawa,

Sei,

Tahara

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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In recent years, systems that collect location information and publish statistics, such as those that publish congestion information, have been extensively employed. Because it is possible to infer an individual’s identity even if the information is not directly disclosed, it is essential to disclose data with privacy protection. Therefore, privacy protection methods based on differential privacy are attracting attention. Geo-indistinguishability is the most famous extension theorem of differential privacy for location information. Geo-indistinguishability can be achieved by adding noise to a target value that must be protected. However, noise addition reduces the usefulness of the data. Thus, it is desirable to add minimal noise to your privacy budget. Therefore, we focus on the fact that the values obtained using measurement devices contain errors. We introduced a novel concept of differential privacy tailored for location information, termed true-value-based geo-indistinguishability (T-Geo-I), which accounts for equipment noise. We also proposed a location information privacy protection method that considers T-Geo-I and reduces the amount of added noise. The object of privacy protection should be the “true value” not the “measured value” that includes measurement errors. According to the experimental results, in the case wherein the measurement error is the normal distribution, our method reduced the noise average and mean square error (MSE) by up to 41% and 63%, respectively, compared with conventional methods while maintaining a prespecified level of privacy in 108 samples of numerical data. In the case wherein the measurement error is the lognormal distribution, the proposed method based on T-Geo-I succeeded in reducing the noise average and MSE by up to 60% and 67%, respectively, compared with methods based on Geo-I, while maintaining a prespecified level of privacy. These findings indicate that the proposed method can improve the usefulness of data while maintaining a prespecified degree of privacy protection.

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Section: Overview Of Location Privacy Researchmentioning

confidence: 99%