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

SaTC：核心：小型：针对相关数据的稳健、隐私和实用性保护指纹方案

基本信息

批准号：
2050410
负责人：
Erman Ayday
金额：
$ 50万
依托单位：
Case Western Reserve University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050410&HistoricalAwards=false
关键词：
SaTC CORE Small Robust Privacy

项目摘要

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.

在当今的数据驱动世界中，要获得个性化服务或为科学研究做出贡献，数据所有者与大量服务提供商（SP）共享敏感信息。在这样做的同时，他们想确保SPS将遵守数据使用协议，而不是未经授权的数据共享。如果未经授权的数据分布，数据所有者希望检测（并确定）此类数据泄漏的来源，以使相应的SP（S）承担责任。数字指纹识别是一种通过将唯一标记（称为指纹）嵌入共享对象的技术来识别数字对象的接收者，目的是确定负责数据泄漏的有罪SP。 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指纹的鲁棒性。对于指纹相关的数据，这种独特的挑战需要从根本上设计设计指纹算法的新方法，这些算法也提供了高数据实用性和数据隐私。在这项研究中，研究人员提出了针对相关数据的鲁棒，隐私和公用事业的指纹识别的新技术。首先，现有指纹方案对攻击的脆弱性将通过利用数据中的相关性来显示。为了减轻所确定的漏洞，将开发出可为广泛攻击提供鲁棒性的新概率指纹识别算法。此外，意识到提议的指纹算法与隐私数据共享之间的相似之处首次，提议的技术将在共享数据时提供隐私和强大的指纹。具体而言，拟议的研究推力包括：（i）对拟议的概率指纹识别算法的深入研究，包括正式的鲁棒性分析，研究不同的相关模型以及考虑不同的效用定义的效用；（ii）针对不同数据类型（例如个人相关的数据，数据库和图形）应用所提出的指纹方案；（iii）开发数据共享指标和算法，这些指标和算法通过探索差异隐私及其变体来提供隐私以及强大的指纹识别；（iv）开发算法以找到最佳的数据处理顺序，这些算法仅优化指纹鲁棒性，隐私和实用性。从更广泛的角度来看，调查人员期望拟议的研究在多个领域的影响很大：（i）对社会，通过提供识别未经授权数据泄漏来源的工具，并具有很高的可能性。这将从未经授权的用户数据共享中确定恶意SP。此外，数据所有者知道他们对如何使用和共享数据具有强大的控制权，将更愿意与SPS共享他们的数据；（ii）通过培训毕业生，本科和高中生在教育和学习方面；（iii）关于通过招募妇女和代表性不足的群体在计算中扩大代表性不足的群体的参与。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估NSF的法定任务。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Towards Robust Fingerprinting of Relational Databases by Mitigating Correlation Attacks

DOI：
10.1109/tdsc.2022.3191117
发表时间：
2023-07
期刊：
IEEE Transactions on Dependable and Secure Computing
影响因子：
7.3
作者：
Tianxi Ji;Erman Ayday;Emre Yilmaz;Pan Li
通讯作者：
Tianxi Ji;Erman Ayday;Emre Yilmaz;Pan Li

The Curse of Correlations for Robust Fingerprinting of Relational Databases

DOI：
10.1145/3471621.3471853
发表时间：
2021-03
期刊：
Proceedings of the 24th International Symposium on Research in Attacks, Intrusions and Defenses
影响因子：
0
作者：
Tianxi Ji;Emre Yilmaz;Erman Ayday;Pan Li
通讯作者：
Tianxi Ji;Emre Yilmaz;Erman Ayday;Pan Li

Privacy-Preserving and Robust Watermarking on Sequential Genome Data using Belief Propagation and Local Differential Privacy