NSF CRII: SaTC: Fingerprinting Encrypted Voice Traffic on Smart Speakers

Project Information

• Award Number: CNS-1947913

• Award Amount: $207,000

• Principal Investigator: Dr. Boyang Wang

• Project Period: 03/01/2020 – 02/28/2023

• Graduate Students: Chenggang Wang, Haipeng Li, Hao Liu, Jimmy Dani

• Undergrad Students: Shane Reilly, Austen Brownfield

Project Overview

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).

Publications

1. Hao Liu, Jimmy Dani, Hongkai Yu, Wenhai Sun, and Boyang Wang
   “AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples”
   IEEE/ACM International Symposium on Quality of Service (IWQoS 2022), Virtual, June 10–12, 2022.
   Code

2. Haipeng Li, Nan Niu and Boyang Wang
   “Cache Shaping: An Effective Defense Against Cache-Based Website Fingerprinting”
   ACM Conference on Data and Application Security and Privacy (ACM CODASPY 2022), Baltimore MD, April 24–27, 2022.
   Code & Datasets

3. Haipeng Li, Chenggang Wang, Nirnimesh Ghose, Boyang Wang,
   “POSTER: Robust Deep-learning-based Radio Fingerprinting with Fine-Tuning,”
   ACM Conference on Security and Privacy in Wireless and Mobile Network (ACM WiSec 2021), Virtual, June, 2021.
   Code & Datasets

4. Jimmy Dani and Boyang Wang
   “HiddenText: Cross-Trace Website Fingerprinting over Encrypted Traffic,”
   IEEE Conference on Information Reuse and Integration for Data Science (IEEE IRI 2021), Virtual, August 10-12, 2021
   Code & Datasets

5. Chenggang Wang, Jimmy Dani, Xiang Li, Xiaodong Jia and Boyang Wang
   “Adaptive Fingerprinting: Website Fingerprinting over Few Encrypted Traffic,”
   ACM Conference on Data and Application Security and Privacy (ACM CODASPY 2021), Virtual, April 26–28, 2021.
   Code & Datasets

6. Haipeng Li, Ben Niu and Boyang Wang
   “SmartSwitch: Efficient Traffic Obfuscation against Stream Fingerprinting,”
   International Conference on Security and Privacy in Communication Networks (SecureComm 2020), Virtual, October 21–23, 2020. (Best paper award runner-up)
   Code & Datasets

7. Chenggang Wang, Sean Kennedy, Haipeng Li, King Hudson, Gowtham Atluri, Xuetao Wei, Wenhai Sun, Boyang Wang,
   “Fingerprinting Encrypted Voice Traffic on Smart Speakers with Deep Learning,”
   ACM Conference on Security and Privacy in Wireless and Mobile Network (ACM WiSec 2020), Virtual, July 8-10, 2020. (Acceptance rate: 27/104=26%) (The first two authors contribute equally in this paper)
   Code & Datasets, Teaser Video, Preprint Version

8. Sean Kennedy, Haipeng Li, Chenggang Wang, Hao Liu, Boyang Wang and Wenhai Sun,
   “I Can Hear Your Alexa: Voice Command Fingerprinting on Smart Home Speakers,”
   IEEE Conference on Communication and Network Security (CNS 2019), 2019 (Acceptance rate: 32/115=28%) (The first three authors contribute equally in this paper)
   Code and Datasets