Side-Channel Attacks and New Principles in the Shuffle Model of Differential Privacy
Shaowei Wang, Jin Li, Changyu Dong, Jin Li, Zhili Zhou, Di Wang, Zikai Alex Wen · IEEE Transactions on Information Forensics and Security · 2025
The shuffle model employs a shuffler to anonymize and permute user messages, thereby enhancing privacy/utility trade-offs compared to the local model. Ideally, it assumes perfect message anonymity protection against adversaries, allowing each user to hide among a large population. However, in contexts like mobile/edge networks or in scenarios where the shuffler is curious, this assumption is frequently unrealistic. In this study, we demonstrate the vulnerability of the shuffle model to communication side-channel attacks, which substantially compromise privacy amplification via shuffling. We categorize side-channel information in the shuffle model into three types: (i) in-out information, revealing the victim user’s participation and timing, (ii) message-cardinality information, indicating the victim’s message count, and (iii) message-length information, disclosing the victim’s message length(s). Numerical results indicate these attacks increase privacy loss by 200% to 4100%, revealing secret value with probability more than 90%. After theoretically analyzing the remaining privacy amplification effects, we suggest several countermeasures and principles to alleviate degradation caused by these attacks: (a) appending padding bits to each message to counter message-length attacks, (b) maximizing query parallelization to elude in-out attacks and increase the population for privacy amplification, and (c) sending dummy messages to exchange communication costs for improved privacy amplification effects. The newly proposed paradigms and principles significantly save privacy budget in comparison to current models under attack.