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: 1704287
• 负责人: Zhipeng Cai
• 金额: $ 27.89万
• 依托单位: Georgia State University Research Foundation, Inc.
• 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=1704287&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，人们和他们的联系人经常共享个人信息，例如人口统计、兴趣和观点，并在网络中留下他们与他人互动的痕迹和内容。不是每个人都想分享个人信息;然而，人们的属性彼此相关，与网络中附近人的属性相关，以及一个人在不同网络上的帐户之间的属性相关。这些相关性在推断人们宁愿保密的属性方面带来了风险。 该项目将尝试通过开发新的方法来识别和量化风险，通过利用这些相关性来推断属性，然后在两个常见的社交网络任务中开发防御机制。 为了查询广告和研究中常用的社交网络数据集，研究人员将为网络开发新的差分隐私技术，以确保查询结果不会无意中识别个人用户或其属性。 为了匹配推荐系统中经常使用的社交网络配置文件，该团队将开发新的相似性匹配方法，该方法适用于加密的个人数据。 总的来说，这项研究将使人们更深入地了解个人信息意外泄露的风险，以及解决这些风险的可能技术和政策方法。该项目还将为三个机构的研究生和本科生提供研究机会，研究团队将强调从大学和高中阶段的计算历史上代表性不足的群体中招募学生。该项目围绕三个主要目标展开。第一个重点是开发对社交网络中用户属性和身份的推理攻击。为此，该团队将首先根据不同属性在网络中的分布计算其相对区分能力，然后使用此信息和网络结构信息通过亲和传播进行属性推断。第二个重点是改进图查询的差分隐私保护。为此，该团队将定义相似性度量，该度量考虑了社交网络中边缘的非独立性，以更好地保护属性隐私，并基于子图划分和考虑查询函数的敏感性开发新的查询技术。他们还将开发基于k-匿名的差分隐私的新变体，隐藏用户相对于相似用户的属性。第三个重点是探索如何在不泄露敏感个人信息的情况下进行配置文件匹配，灵感来自安全多方计算。 在这里，该团队将开发有效和准确的方法来对受基于混沌的加密保护的数据进行点积计算，并对受基于属性集的加密保护的数据进行关键字搜索，以及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。 该团队将通过一个专门的项目网站发布其代码，适当保护的数据集，教程和教育材料，并通过McNair计划，计算机科学女性，西班牙裔专业工程师协会和全国黑人工程师协会向代表性不足的群体成员进行宣传。

项目成果

A Fair Mechanism for Private Data Publication in Online Social Networks

• DOI: 10.1109/tnse.2018.2801798
• 发表时间: 2020-04
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Xu Zheng;Guangchun Luo;Zhipeng Cai

Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

工业物联网中多方的隐私保护数据共享

• DOI: 10.1109/jsac.2020.2980802
• 发表时间: 2020-05-01
• 期刊: IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
• 影响因子: 16.4
• 作者: Zheng, Xu;Cai, Zhipeng
• 通讯作者: Cai, Zhipeng

Search locations safely and accurately: A location privacy protection algorithm with accurate service

• DOI: 10.1016/j.jnca.2017.12.002
• 发表时间: 2018-02
• 期刊: J. Netw. Comput. Appl.
• 作者: Yan Huang;Zhipeng Cai;A. Bourgeois

Privacy protection among three antithetic-parties for context-aware services

• DOI: 10.1016/j.jnca.2021.103115
• 发表时间: 2021-10
• 期刊: J. Netw. Comput. Appl.
• 作者: Yan Huang;Wei Li;Jinbao Wang;Zhipeng Cai;A. Bourgeois

Audio-Visual Autoencoding for Privacy-Preserving Video Streaming

用于保护隐私的视频流的视听自动编码

• DOI: 10.1109/jiot.2021.3089080
• 发表时间: 2021
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Xu, Honghui;Cai, Zhipeng;Takabi, Daniel;Li, Wei
• 通讯作者: Li, Wei