SaTC: CORE: Small: Robust, Privacy- and Utility-Preserving Fingerprinting Schemes for Correlated Data

在当今数据驱动的世界中，为了获得个性化服务或为科学研究做出贡献，数据所有者与广泛的服务提供商（sp）共享敏感信息。在这样做的同时，他们希望确保服务提供商遵守数据使用协议，不参与未经授权的数据共享。因此，在未经授权分发其数据的情况下，数据所有者希望检测（并识别）此类数据泄漏的来源，以使相应的服务提供商承担责任。数字指纹是一种通过在共享对象中嵌入唯一标记（称为指纹）来识别数字对象接收者的技术，目的是识别负责数据泄露的有罪SP。然而，现有的指纹识别技术并不直接适用于共享敏感、相关和高价值（就实用性而言）的数据，因为(i)它们（特别是多媒体数据）利用数据中的高冗余，（ii）嵌入的标记需要很大以提供抗攻击的鲁棒性，这降低了共享数据的实用性，以及（iii）它们不考虑数据点之间的相关性，这降低了指纹的鲁棒性。指纹识别相关数据的这种独特挑战需要从根本上设计新的方法来设计指纹识别算法，同时提供高数据效用和数据隐私。在这项研究中，研究人员提出了一种新的技术，可以对相关数据进行稳健、隐私和实用的指纹识别。首先，通过利用数据中的相关性来显示现有指纹识别方案对攻击的脆弱性。为了减轻已识别的漏洞，将开发新的概率指纹识别算法，以提供针对各种攻击的鲁棒性。此外，认识到所提出的指纹识别算法与保护隐私的数据共享之间的相似性，所提出的技术将首次在共享数据的同时提供隐私和健壮的指纹识别。具体而言，建议的研究重点包括：(1)深入研究所提出的概率指纹算法，包括形式鲁棒性分析，研究不同的相关模型，并考虑不同的效用定义来提高效用；（ii）为不同类型的数据（例如个人相关数据、数据库和图表）应用拟议的指纹识别方案；（iii）开发数据共享指标和算法，通过探索差异隐私及其变体来提供隐私和强大的指纹识别；（iv）开发算法来找到数据处理的最佳顺序，同时优化指纹的鲁棒性、隐私性和实用性。从更广泛的角度来看，研究人员期望拟议的研究在几个领域产生重大影响：(i)通过提供识别高概率未经授权的数据泄露来源的工具，对社会产生重大影响。这将阻止恶意服务提供商未经授权共享其用户数据。此外，数据所有者知道他们对数据的使用和共享有更强的控制权，将更愿意与服务提供商分享他们的数据；（二）在教育和学习方面，通过培训研究生、本科生和高中生；（三）通过在本项目中招募妇女和代表性不足的群体，扩大代表性不足群体在计算机领域的参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In today's data-driven world, to receive personalized services or contribute to scientific studies, data owners share sensitive information with a wide-range of service providers (SPs). While doing so, they want to make sure that SPs will comply with the data usage agreements and not engage in unauthorized sharing of their data. Thus, in case of an unauthorized distribution of their data, data owners want to detect (and identify) the source of such data leakages to keep the corresponding SP(s) liable. Digital fingerprinting is a technique to identify the recipient of a digital object by embedding a unique mark (called fingerprint) into the shared object, with the aim to identify the guilty SP who is responsible for data leakage. However, existing fingerprinting techniques are not directly applicable for sharing sensitive, correlated, and high value (in terms of utility) data because (i) they (especially for multimedia data) utilize the high redundancy in the data, (ii) the embedded marks need to be large to provide robustness against attacks, which reduces the utility of shared data, and (iii) they do not consider the correlations between data points, which reduces the robustness of the fingerprint. Such unique challenges for fingerprinting correlated data require fundamentally new ways to design fingerprinting algorithms that also provide high data utility and data privacy. In this research, the investigators propose novel techniques for robust, privacy- and utility-preserving fingerprinting of correlated data. First, the vulnerability of existing fingerprinting schemes to the attacks will be shown by exploiting the correlations in the data. To mitigate the identified vulnerabilities, new probabilistic fingerprinting algorithms that provide robustness against a wide-variety of attacks will be developed. Furthermore, realizing the similarities between the proposed fingerprinting algorithms and privacy-preserving data sharing, for the first time, the proposed techniques will provide both privacy and robust fingerprinting while sharing data. Specifically, the proposed research thrusts include: (i) in-depth study of the proposed probabilistic fingerprinting algorithms, including formal robustness analysis, studying different correlation models, and improving utility considering different utility definitions; (ii) application of the proposed fingerprinting schemes for different data types, such as personal correlated data, databases, and graphs; (iii) developing data sharing metrics and algorithms that provide privacy along with robust fingerprinting by exploring differential privacy and its variants; and (iv) developing algorithms to find the optimal order of data processing that simultaneously optimize fingerprint robustness, privacy, and utility. In a broader view, the investigators expect the impact of the proposed research to be significant in several areas: (i) on society, by providing tools that identify the sources of unauthorized data leakages with high probability. This will deter malicious SPs from unauthorized sharing of their users’ data. Furthermore, data owners, knowing they have stronger control on how their data will be used and shared, will be more willing to share their data with the SPs; (ii) on education and learning, by training graduate, undergraduate, and high school students; and (iii) on broadening participation of underrepresented groups in computing, by recruitment of women and underrepresented groups in this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.