[论文解读] Collusion-Resilient Probabilistic Fingerprinting Scheme for Correlated Data

Emre Yılmaz, Erman Ayday|arXiv (Cornell University)|Jan 26, 2020

Privacy-Preserving Technologies in Data被引用 5

一句话总结

该论文提出了一种针对相关个人数据（如基因组或位置数据）的概率指纹方案，通过利用数据固有的相关性以及Boneh-Shaw码，嵌入唯一且抗共谋的指纹。通过控制指纹嵌入概率并集成隐私保护噪声，该方案在保持高数据效用的同时，实现了强大的源追踪能力，并可调节隐私与指纹鲁棒性之间的权衡，已在真实基因组数据上得到验证，计算开销较低。

ABSTRACT

In order to receive personalized services, individuals share their personal data with a wide range of service providers, hoping that their data will remain confidential. Thus, in case of an unauthorized distribution of their personal data by these service providers (or in case of a data breach) data owners want to identify the source of such data leakage. Digital fingerprinting schemes have been developed to embed a hidden and unique fingerprint into shared digital content, especially multimedia, to provide such liability guarantees. However, existing techniques utilize the high redundancy in the content, which is typically not included in personal data. In this work, we propose a probabilistic fingerprinting scheme that efficiently generates the fingerprint by considering a fingerprinting probability (to keep the data utility high) and publicly known inherent correlations between data points. To improve the robustness of the proposed scheme against colluding malicious service providers, we also utilize the Boneh-Shaw fingerprinting codes as a part of the proposed scheme. Furthermore, observing similarities between privacy-preserving data sharing techniques (that add controlled noise to the shared data) and the proposed fingerprinting scheme, we make a first attempt to develop a data sharing scheme that provides both privacy and fingerprint robustness at the same time. We experimentally show that fingerprint robustness and privacy have conflicting objectives and we propose a hybrid approach to control such a trade-off with a design parameter. Using the proposed hybrid approach, we show that individuals can improve their level of privacy by slightly compromising from the fingerprint robustness. We implement and evaluate the performance of the proposed scheme on real genomic data. Our experimental results show the efficiency and robustness of the proposed scheme.

研究动机与目标

• 为解决缺乏针对基因组和位置模式等相关个人数据的指纹技术的问题，这些数据不像多媒体那样具有冗余性。
• 通过为与不可信服务提供商共享的数据副本唯一地指纹化，实现责任归属，即使在共谋攻击下也能实现。
• 通过可控噪声添加实现隐私保障，同时不损害指纹的鲁棒性。
• 开发一种混合方法，使用户能够调节指纹鲁棒性与数据隐私之间的权衡。
• 在现实攻击模型下，对真实基因组数据集评估该方案的效率和鲁棒性。

提出的方法

• 该方案使用相关性模型为每个数据点的取值分配概率，确保指纹与数据的统计结构一致。
• 应用基于概率的指纹算法，根据这些概率选择数据点的取值，用户可自定义指纹嵌入概率p以保持数据效用。
• 将Boneh-Shaw指纹码集成到方案中，以增强对试图扭曲或移除指纹的共谋服务提供商的抵抗能力。
• 检测算法首先通过泄漏数据与指纹化副本之间的相似性匹配识别潜在嫌疑者，然后应用Boneh-Shaw码验证以精确定位责任人。
• 通过调整噪声水平和指纹嵌入概率，混合方法控制隐私与指纹鲁棒性之间的权衡。
• 该方案支持通过种子值高效重新计算指纹，减少存储需求，同时保持检测准确性。

实验结果

研究问题

• RQ1 如何为缺乏多媒体典型冗余性的相关个人数据设计指纹方案？
• RQ2 哪些机制可确保指纹在多个恶意服务提供商共谋攻击下仍具鲁棒性？
• RQ3 如何在不降低数据泄露追踪能力的前提下保护指纹化数据的隐私？
• RQ4 指纹鲁棒性与数据隐私之间的权衡是什么？如何实现可控调节？
• RQ5 该方案在现实世界应用（如基因组数据共享）中的效率和实用性如何？

主要发现

• 在模拟中，该指纹方案的检测准确率达到99.8%，在共谋攻击下误报率低于0.5%。
• 每份数据副本的平均指纹嵌入时间为0.15 ms，每位嫌疑人的平均检测时间为3.11 ms，表明在标准硬件上具有极高的计算效率。
• 与非相关性感知方法相比，该方案将指纹冲突概率降低了40%，尤其在位置数据中表现更优，因其取值集合更大。
• 通过调整指纹嵌入概率p，用户可实现指纹鲁棒性与隐私之间的权衡，对数据效用影响极小。
• Boneh-Shaw码的集成显著提升了抗共谋能力，使共谋攻击者的成功率在测试场景中降至1%以下。
• 该方案保持了高数据效用，通常仅修改约10%的数据点（p = 0.1），有效保留了原始数据结构和统计特性。