DeepLeak: 通过模型解释增强隐私保护以对抗成员泄露
DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
AI总结：
DeepLeak通过系统化方法减少模型解释中的成员泄露，提升隐私保护与可解释性的平衡。
摘要：
机器学习（ML）可解释性在高风险领域如预测诊断和贷款审批中至关重要。然而，这些领域同样需要严格的隐私保障，这在可解释性和隐私之间产生了矛盾。尽管已有研究显示解释方法会泄露成员信息，但从业者仍然缺乏系统指导来选择或部署平衡透明度与隐私的解释技术。我们提出了DeepLeak，一个用于审计和缓解后处理解释方法隐私风险的系统。DeepLeak在三个方面取得了进展：（1）全面的泄露分析：我们开发了更强的解释感知成员推断攻击（MIA）以量化默认配置下代表性解释方法泄露成员信息的程度；（2）轻量级加固策略：我们引入了实用且模型无关的缓解措施，包括灵敏度校准的噪声、属性剪裁和遮蔽，这些措施显著减少了成员泄露同时保持解释效用；（3）根本原因分析：通过受控实验，我们确定了驱动泄露的算法属性（例如属性稀疏性和灵敏度）。在图像基准上评估15种解释技术，DeepLeak显示默认设置会比之前报告的泄露多达74.9%的成员信息。我们的缓解措施通过减少泄露最多95%（最低46.5%），同时平均仅导致3.3%的效用损失。DeepLeak提供了一条系统且可复现的安全解释路径，以在隐私敏感的ML中实现更安全的可解释性。
英文摘要：
Machine learning (ML) explainability is central to algorithmic transparency in high-stakes settings such as predictive diagnostics and loan approval. However, these same domains require rigorous privacy guaranties, creating tension between interpretability and privacy. Although prior work has shown that explanation methods can leak membership information, practitioners still lack systematic guidance on selecting or deploying explanation techniques that balance transparency with privacy. We present DeepLeak, a system to audit and mitigate privacy risks in post-hoc explanation methods. DeepLeak advances the state-of-the-art in three ways: (1) comprehensive leakage profiling: we develop a stronger explanation-aware membership inference attack (MIA) to quantify how much representative explanation methods leak membership information under default configurations; (2) lightweight hardening strategies: we introduce practical, model-agnostic mitigations, including sensitivity-calibrated noise, attribution clipping, and masking, that substantially reduce membership leakage while preserving explanation utility; and (3) root-cause analysis: through controlled experiments, we pinpoint algorithmic properties (e.g., attribution sparsity and sensitivity) that drive leakage. Evaluating 15 explanation techniques across four families on image benchmarks, DeepLeak shows that default settings can leak up to 74.9% more membership information than previously reported. Our mitigations cut leakage by up to 95% (minimum 46.5%) with only <=3.3% utility loss on average. DeepLeak offers a systematic, reproducible path to safer explainability in privacy-sensitive ML.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
