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Related Experiment Videos

SLeak: Multi-Target Privacy Stealing Attack Against Split Learning.

Xiaoyang Xu, Wenzhe Yi, Juan Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    Split Learning (SL) is vulnerable to privacy attacks. A new threat, Split Leakage (SLeak), exploits client representation preferences to steal data and functionality, even with limited public data.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning Security
    • Distributed Systems

    Background:

    • Split Learning (SL) offers privacy and efficiency but faces inference attack risks.
    • Existing SL privacy defenses rely on unrealistic assumptions, limiting real-world effectiveness.
    • Server adversaries can potentially compromise client privacy in SL frameworks.

    Purpose of the Study:

    • To investigate inherent vulnerabilities in Split Learning (SL) frameworks.
    • To introduce a novel privacy threat, Split Leakage (SLeak), against SL.
    • To demonstrate SLeak's effectiveness without strong privacy assumptions.

    Main Methods:

    • Analyzing client representation preferences in SL's smashed data and server model.
    • Developing a substitute client to mimic target client behavior.

    Related Experiment Videos

  • Introducing the Split Leakage (SLeak) threat for multiple privacy objectives.
  • Utilizing partial same-domain auxiliary public data for attacks.

    • Main Results:

    • Identified that both smashed data and server models reveal client representation preferences.
    • Demonstrated that a substitute client can perfectly replicate target client functionality, data, and labels.
    • SLeak attacks showed superior performance compared to state-of-the-art methods across diverse datasets and models.
    • Ablation studies confirmed SLeak's robustness and applicability in various scenarios.

    Conclusions:

    • Split Learning (SL) possesses inherent vulnerabilities exploitable by server adversaries.
    • Split Leakage (SLeak) presents a practical and effective threat to SL privacy.
    • SLeak's success with minimal data requirements highlights significant privacy risks in distributed learning.