[论文解读] Towards Differentially Private Truth Discovery for Crowd Sensing Systems

Yaliang Li, Houping Xiao+6 more|arXiv (Cornell University)|Oct 10, 2018

Mobile Crowdsensing and Crowdsourcing参考文献 23被引用 10

一句话总结

该论文提出了一种用于众包感知系统的差分隐私真实性发现机制，通过本地数据扰动保护用户隐私，同时保持高聚合精度。每位用户独立地从指数分布中抽取方差参数 δ²，并向其传感数据添加均值为零、方差为 δ² 的高斯噪声；服务器随后基于用户权重的估计值执行加权聚合，通过本地差分隐私实现强隐私保障，并在高噪声水平下仍保持鲁棒的实用性。

ABSTRACT

Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significantly among different users. To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic. Although the existing truth discovery approaches can provide reliable aggregated results, they fail to protect the private information of individual users. Moreover, crowd sensing systems typically involve a large number of participants, making encryption or secure multi-party computation based solutions difficult to deploy. To address these challenges, in this paper, we propose an efficient privacy-preserving truth discovery mechanism with theoretical guarantees of both utility and privacy. The key idea of the proposed mechanism is to perturb data from each user independently and then conduct weighted aggregation among users' perturbed data. The proposed approach is able to assign user weights based on information quality, and thus the aggregated results will not deviate much from the true results even when large noise is added. We adapt local differential privacy definition to this privacy-preserving task and demonstrate the proposed mechanism can satisfy local differential privacy while preserving high aggregation accuracy. We formally quantify utility and privacy trade-off and further verify the claim by experiments on both synthetic data and a real-world crowd sensing system.

研究动机与目标

• 解决现有众包感知系统中真实性发现机制缺乏用户隐私保护的问题。
• 开发一种高效、可扩展的解决方案，无需用户之间的协调或加密。
• 通过本地差分隐私实现理论上的隐私保障，同时在聚合结果中保持高实用性。
• 在用户数据可能包含敏感信息的大规模系统中，实现准确的真实性发现。

提出的方法

• 每位用户从指数分布中采样一个噪声方差参数 δ²，并向其传感数据添加均值为零、方差为 δ² 的高斯噪声。
• 将扰动后的数据发送至中央服务器，服务器基于用户权重的估计值执行加权聚合。
• 用户权重通过迭代方式推导，依据其扰动后数据与聚合结果的一致性程度，反映信息质量。
• 利用 (α,β)-实用性与 (ε,δ)-隐私定义对机制进行形式化分析，量化准确率与隐私之间的权衡。
• 理论分析表明，更大的噪声可提升隐私保护，但会降低实用性，且在样本量较大时具备收敛性保障。
• 该方法利用本地差分隐私，使每位用户可独立扰动数据，无需协调。

实验结果

研究问题

• RQ1 是否存在一种真实性发现机制，可在不依赖安全多方计算或加密的前提下，保护众包感知系统中的用户隐私？
• RQ2 当数据因隐私保护而被添加噪声时，如何有效估计用户权重？
• RQ3 在扰动的真实性发现中，实用性（准确率）与隐私（差分隐私参数）之间的理论权衡是什么？
• RQ4 即使对单个用户数据添加了大量噪声，是否仍能保持高聚合精度？

主要发现

• 所提出的机制在理论上满足本地差分隐私，确保强用户隐私保护。
• 即使在大噪声水平下，聚合精度也仅轻微下降，表明在隐私约束下具备高实用性。
• 当 c=1 时，在 α 和 λ₁ 满足特定条件时，随着样本数 S 趋近于无穷大，聚合结果的期望误差收敛至零。
• 理论分析证实，增加噪声可增强隐私保护，但会降低实用性，且可通过 (α,β)-实用性与 (ε,δ)-隐私度量实现可量化的权衡。
• 在合成数据及真实世界的室内楼层图构建系统上的实验结果验证了该机制在高噪声水平下的鲁棒性能。