SaTC: CORE: Small: Securing Network Embedding against Privacy Attacks

许多复杂系统采用网络的形式，如社会网络、生物网络和信息网络。众所周知，在大型网络上执行数据分析是具有挑战性的。为了应对这一挑战，人们设计了许多网络表示学习（NRL）方法来学习保存网络信息的低维向量表示（嵌入）。由于其优势，网络嵌入不是将原有的网络数据向公众发布，而是与第三方机器学习服务提供商或其他方共享，用于下游分析。由于网络嵌入固有地捕获了原始网络的结构和性质，因此嵌入是否也编码了输入网络数据中的敏感信息引起了人们的严重关注。攻击者可以对网络嵌入进行各种隐私攻击，以推断输入网络数据中的敏感信息。尽管机器学习隐私方面取得了重大进展，但大多数现有的隐私攻击主要集中在非图形数据（表格数据、文本和图像）上训练的模型上。NRL模型及其输出网络嵌入的隐私性在很大程度上被忽略了。对于网络嵌入针对隐私攻击的漏洞的性质和程度的认识，以及对有效防御机制的研究都非常有限。该项目将通过一个统一的研究计划来解决网络嵌入的核心隐私问题，该计划包括探索网络嵌入的隐私漏洞，调查这些漏洞的原因，并开发严格而实用的技术来减轻这些漏洞。项目拟解决三个基础研究问题：(1)设计三种类型的隐私攻击，即隶属推理攻击、属性推理攻击和属性推理攻击，从网络嵌入中推断出原始网络数据中敏感的隶属/属性/属性；（ii）分析哪些类型的数据特征和模型属性会影响网络嵌入的隐私漏洞，以应对这三种类型的攻击；（三）设计有效的防御机制，保证网络嵌入的安全。该项目的研究成果将通过开发新课程，让不同层次的学生参与机器学习的前沿研究，以及培训女性和代表性不足的学生来广泛传播。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。

Many complex systems take the form of networks, such as social networks, biological networks, and information networks. It is well recognized that performing data analysis on large networks is challenging. To tackle this challenge, numerous network representation learning (NRL) approaches have been designed to learn low-dimensional vector representations (embedding) that preserve the network information. Due to their advantages, network embedding, instead of the original network data, is either released to the public or shared with third-party machine learning service providers or other parties for downstream analytics. Since the network embedding inherently captures the structure and properties of the original network, it raises the serious concern whether the embedding also encodes the sensitive information in the input network data. An adversary can launch various privacy attacks on the network embedding to infer the sensitive information in the input network data. Despite significant progress in machine learning privacy, most of the existing privacy attacks mainly focus on the models trained on non-graph data (tabular data, text, and images). Privacy of NRL models and their output network embeddings are largely ignored. The understanding of the nature and extent of vulnerabilities of network embedding against the privacy attacks as well as the investigation of effective defense mechanisms are extremely limited. This project will address the core privacy issues of network embedding through a unified research program that consists of exploring the privacy vulnerabilities of network embedding, investigating the causes of these vulnerabilities, and developing rigorous, yet practical, techniques to mitigate these vulnerabilities. The project plans to tackle three fundamental research problems: (i) design three types of privacy attacks, namely membership inference attacks, attribute inference attacks, and property inference attacks, to infer the sensitive membership/attributes/properties in the original network data from network embedding; (ii) analyze which types of data characteristics and model properties impact the privacy vulnerabilities of network embedding against the three types of attacks; and (iii) design effective defense mechanisms to secure network embedding. The research outcomes of this project will be disseminated broadly through developing new courses, involving students at various levels into cutting-edge research in machine learning, and training of female and underrepresented students.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.