On Privacy Risks of Watching YouTube over Cellular Networks with Carrier Aggregation
Nitya Lakshmanan, Abdelhak Bentaleb, Byoungjun Choi, Roger Zimmermann, Jun Han, Min Suk Kang
Abstract
One's core values, personality, and social status may be reflected in the watch history of online video streaming services such as YouTube. Unfortunately, several prior research work demonstrates that man-in-the-middle or malware-assisted attackers can accurately infer the titles of encrypted streaming videos by exploiting the inherent correlation between the encoded video contents and the traffic rate changes. In this paper, we present a novel video-inference attack called Moba that further exacerbates the problem by only requiring the adversary to simply eavesdrop the broadcast messages of a primary cell of a targeted user's cellular phone. Our attack utilizes a side channel in modern cellular networks that leaks the number of actively transmitting cells for each user. We show that this seemingly harmless system information leakage can be used to achieve practical video-inference attacks. To design effective video-inference attacks, we augment the coarse-grained side-channel measurements with precise timing information and estimate the traffic bursts of encrypted video contents. The Moba attack considers an adversary-chosen set of suspect YouTube videos, from which a targeted user may watch some videos during the attack. We confirm the feasibility of Moba in identifying the exact YouTube video title (if it is from the suspect set) via our over-the-air experiments conducted in LTE-Advanced networks in two countries. Moba can be effective in verifying whether a targeted user watches any of the suspect videos or not; e.g., precision of 0.98 is achieved after observing six-minutes of a single video play. When further allowed to observe multiple video plays, Moba adversary is able to identify whether the targeted user frequently watches the suspect videos with a probability close to one and a near-zero false positive rate. Finally, we present a simple padding-based countermeasure that significantly reduces the attack effectiveness without sacrificing any cellular radio resources.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Cited by top-tier papers1
Ask how each one uses itRelated papers
- Watching the Watchers: Practical Video Identification Attack in LTE NetworksSangwook Bae, Mincheol Son, Dongkwan Kim, CheolJun Park et al.USENIX Security 2022
- Endangered Privacy: Large-Scale Monitoring of Video Streaming ServicesMartin Björklund, Romaric DuvignauUSENIX Security 2025
- Beauty and the Burst: Remote Identification of Encrypted Video StreamsRoei Schuster, Vitaly Shmatikov, Eran TromerUSENIX Security 2017 · 205 citations
- SnailLoad: Exploiting Remote Network Latency Measurements without JavaScriptStefan Gast, Roland Czerny, Jonas Juffinger, Fabian Rauscher et al.USENIX Security 2024 · 4 citations
- A Stealthy Location Identification Attack Exploiting Carrier Aggregation in Cellular NetworksNitya Lakshmanan, Nishant Budhdev, Min Suk Kang, Mun Choon Chan et al.USENIX Security 2021 · 21 citations
