SaTC: CORE: Medium: Collaborative: Privacy Attacks and Defense Mechanisms in Online Social Networks

SaTC：核心：媒介：协作：在线社交网络中的隐私攻击和防御机制

• 批准号: 1704274
• 负责人: Xiang Chen
• 金额: $ 27.7万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704274&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Collaborative; Privacy

In online social networks, people and their connections often share personal information, such as demographics, interests, and opinions, and leave traces of their interaction with others and content in the network. Not everyone wants to share personal information; however, people's attributes are correlated with each other among themselves, with attributes of nearby people in the network, and between a person's accounts on different networks. These correlations create risks around inferring attributes people would rather keep private. This project will try to identify and quantify the risks by developing new ways to infer attributes by leveraging these correlations, then develop defense mechanisms in two common social networking tasks. For querying social network datasets, which is commonly used in advertising and research, the researchers will develop new differential privacy techniques for networks to ensure that query results do not inadvertently identify individual users or their attributes. For matching social network profiles, which is often used in recommender systems, the team will develop novel similarity matching methods that work on encrypted personal data. Overall, the research will provide a deeper understanding of the risks of inadvertent leakage of personal information and possible technical and policy approaches for addressing those risks. The project will also provide research opportunities for both graduate and undergraduate students at three institutions, and the research team will emphasize recruiting students from historically underrepresented groups in computing both at the college and high school level. The project is organized around three main thrusts. The first thrust is to develop inference attacks on users' attributes and identity in social networks. To do this the team will first compute the relative discriminatory power of different attributes based on their distributions in the network, then use this and network structural information to perform attribute inference through affinity propagation. The second thrust focuses on improving differential privacy protection for graph queries. For this, the team will define similarity metrics that account for the non-independence of edges in social networks to better protect attribute privacy and develop new query techniques based on subgraph partitioning and consideration of the sensitivity of the query function. They will also develop new variants of differential privacy based on k-anonymity that hide a user's attributes relative to those of similar users. The third thrust explores how to do profile matching without revealing sensitive personal information, inspired by ideas from secure multiparty computation. Here, the team will develop efficient and accurate methods to do dot-product computation on data protected by chaos-based encryption and keyword search on data protected by attribute-set-based encryption, as well as hashing-based approaches to compute image similarity without sharing the image data itself. The team will release its code, suitably protected datasets, and tutorials and educational materials through a dedicated project website, and do outreach to members of underrepresented groups through the McNair programs, Women in Computer Science, the Society of Hispanic Professional Engineers, and the National Society of Black Engineers.

在在线社交网络中，人们及其联系经常共享个人信息，例如人口统计学，兴趣和观点，并留下与他人互动的痕迹以及网络中的内容。并非每个人都想分享个人信息；但是，人们的属性彼此之间相互关联，网络中附近人的属性以及一个人在不同网络上的帐户之间的属性。这些相关性围绕着人们宁愿保持私密的属性造成风险。 该项目将尝试通过利用这些相关性来推断属性的新方法来识别和量化风险，然后在两个共同的社交网络任务中开发国防机制。 为了查询通常用于广告和研究中的社交网络数据集，研究人员将为网络开发新的差异隐私技术，以确保查询结果不会无意中识别单个用户或其属性。 对于通常用于推荐系统中的社交网络配置文件，该团队将开发出可用于加密个人数据的新颖相似性匹配方法。 总体而言，这项研究将为您深入了解个人信息无意泄漏的风险以及解决这些风险的技术和政策方法。该项目还将为三个机构的研究生和本科生提供研究机会，研究小组将强调从历史上代表性不足的小组中招募学生在学院和高中一级计算中的学生。该项目围绕三个主要推力组织。首先要做的是开发对用户在社交网络中属性和身份的推理攻击。为此，团队将根据网络中的分布来计算不同属性的相对歧视能力，然后使用此和网络结构信息通过亲和力传播来执行属性推断。第二个推力重点是改善图形查询的差异隐私保护。为此，团队将定义相似性指标，这些指标可以解释社交网络中边缘的非独立性，以更好地保护属性隐私并根据子图分区和考虑查询功能的灵敏度的考虑来开发新的查询技术。他们还将基于k-匿名性开发新的差异隐私变体，这些变体相对于类似用户的属性隐藏了用户的属性。第三个推力探讨了如何在不揭示敏感的个人信息的情况下进行配置文件匹配，这是受安全多方计算的想法的启发。 在这里，团队将开发有效，准确的方法，以对受基于混乱的加密和关键字搜索保护的数据进行DOT - 产品计算，对受属性基于属性的加密保护的数据以及基于哈希的基于哈希的方法来计算图像相似性而无需共享图像数据本身。 该团队将通过专门的项目网站发布其代码，适当保护的数据集以及教程和教育材料，并通过McNair计划，计算机科学妇女，西班牙裔专业工程师协会以及黑人工程师的国家社会与代表性不足的团体的成员进行宣传。

项目成果

CP_ABSC: An attribute-based signcryption scheme to secure multicast communications in smart grids

• DOI: 10.3934/mfc.2018005
• 发表时间: 2018-02
• 期刊: Math. Found. Comput.
• 作者: Chun-qiang Hu;Jiguo Yu;Xiuzhen Cheng;Zhi Tian;Kemal Akkaya;Limin Sun

The Capacity of Multi-user Private Information Retrieval for Computationally Limited Databases

计算受限数据库的多用户私有信息检索能力

• DOI: 10.1109/uemcon51285.2020.9298136
• 发表时间: 2020
• 期刊: Electronics & Mobile Communication Conference (UEMCON
• 作者: Barnhart, William;Tian, Zhi
• 通讯作者: Tian, Zhi

Truthful Auction Analysis and Design in Multiunit Heterogenous Spectrum Markets With Reserve Prices

• DOI: 10.1109/tccn.2020.2996602
• 发表时间: 2021-03
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: Wei Li;Xiuzhen Cheng;Z. Tian;Shengling Wang;R. Bie;Jiguo Yu

Best Effort Voting Power Control for Byzantine-resilient Federated Learning Over the Air

• DOI: 10.1109/iccworkshops53468.2022.9814495
• 发表时间: 2022-05
• 期刊: 2022 IEEE International Conference on Communications Workshops (ICC Workshops)
• 作者: Xin Fan;Yue Wang;Yan Huo;Zhi Tian

Count Sketch with Zero Checking: Efficient Recovery of Heavy Components

• DOI: 10.1109/icassp39728.2021.9413853
• 发表时间: 2021-06
• 期刊: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Guanqiang Zhou;Zhi Tian