CRII: SaTC: Fingerprinting Encrypted Voice Traffic on Smart Speakers

CRII：SaTC：智能扬声器上的指纹加密语音流量

• 批准号: 1947913
• 负责人: Boyang Wang
• 金额: $ 17.5万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947913&HistoricalAwards=false
• 关键词: CRII; SaTC; Fingerprinting; Encrypted; Voice

Millions of users interact with smart speakers every day. However, there remains a significant gap in the understanding of the privacy impacts of smart speakers. A poor understanding of the privacy impacts can lead to unauthorized disclosure, affect the well-beings of users, and thwart Internet freedom. To bridge this gap, this project investigates the privacy leakage of smart speakers under a new encrypted traffic analysis attack, referred to as voice command fingerprinting, and develops new defenses against this attack. This attack infers which voice command a user says to a smart speaker by analyzing side-channel information of encrypted network traffic. This research includes four thrusts: (1) producing large-scale datasets for encrypted traffic analysis on smart speakers; (2) leveraging deep learning in the attack to investigate the privacy leakage; (3) promoting the efficiency of defenses by analyzing which encrypted packets should be protected with a higher priority; (4) developing a defense against the attack by generating adversarial examples on the fly. The research will promote the understanding of the privacy impacts of smart speakers and advance the knowledge in privacy-preserving technologies. The research findings will be disseminated through publications and presentations. The datasets and source code will be made publicly available for the research community. This project will integrate the research activities into curriculum development, render research opportunities to female and underrepresented students, and advance research experience for high school teachers and students in STEM (Science, Technology, Engineering and Math).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.

每天都有数百万用户与智能扬声器互动。然而，在理解智能扬声器对隐私的影响方面仍然存在很大差距。对隐私影响的糟糕理解可能会导致未经授权的披露，影响用户的福祉，并阻碍互联网自由。为了弥合这一差距，该项目调查了一种新的加密流量分析攻击下智能扬声器的隐私泄露，称为语音命令指纹，并开发了针对这种攻击的新防御措施。该攻击通过分析加密网络流量的旁路信息来推断用户向智能扬声器发出的语音命令。这项研究包括四个方面：(1)生成用于智能扬声器加密流量分析的大规模数据集；(2)利用攻击中的深度学习来调查隐私泄露；(3)通过分析哪些加密数据包应该得到更高的优先级来提高防御效率；(4)通过动态生成敌意示例来开发针对攻击的防御。这项研究将促进对智能扬声器对隐私影响的了解，并促进隐私保护技术的知识。研究结果将通过出版物和演示文稿进行传播。数据集和源代码将向研究界公开。该项目将把研究活动整合到课程开发中，为女性和代表性不足的学生提供研究机会，并为高中教师和学生提供STEM(科学、技术、工程和数学)的研究经验。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

RadioNet: Robust Deep-Learning Based Radio Fingerprinting

• DOI: 10.1109/cns56114.2022.9947255
• 发表时间: 2022-10
• 期刊: 2022 IEEE Conference on Communications and Network Security (CNS)
• 作者: Haipeng Li;Kaustubh Gupta;Chenggang Wang;Nirnimesh Ghose;Boyang Wang

Adaptive Fingerprinting: Website Fingerprinting over Few Encrypted Traffic

• DOI: 10.1145/3422337.3447835
• 发表时间: 2021-04
• 期刊: Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy
• 作者: Chenggang Wang;Jimmy Dani;Xiang Li;Xiaodong Jia;Boyang Wang

SmartSwitch: Efficient Traffic Obfuscation Against Stream Fingerprinting

SmartSwitch：针对流指纹识别的高效流量混淆

• 发表时间: 2020
• 期刊: Security and Privacy in Communication Networks. SecureComm 2020
• 作者: Li, Haipeng;Niu, Ben;Wang, Boyang
• 通讯作者: Wang, Boyang

AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples

AdvTraffic：用对抗性示例混淆加密流量

• DOI: 10.1109/iwqos54832.2022.9812875
• 发表时间: 2022
• 期刊: 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS
• 作者: Liu, Hao;Dani, Jimmy;Yu, Hongkai;Sun, Wenhai;Wang, Boyang
• 通讯作者: Wang, Boyang

HiddenText: Cross-Trace Website Fingerprinting over Encrypted Traffic

HiddenText：加密流量的交叉追踪网站指纹

• DOI: 10.1109/iri51335.2021.00044
• 发表时间: 2021
• 期刊: 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI
• 作者: Dani, Jimmy;Wang, Boyang
• 通讯作者: Wang, Boyang