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arXiv 2302.09258cs.CR
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遭受攻击的数字隐私:挑战与促成因素

Digital Privacy Under Attack: Challenges and Enablers

Baobao Song, Shiva Raj Pokhrel, Mengyue Deng, Qiujun Lan, Robin Doss, Gang Li

AI总结:

本文系统梳理数据驱动系统中针对匿名数据、统计聚合和隐私保护模型的隐私攻击及防御措施,分析隐私—效用权衡,并指出差分隐私的实现挑战与未来研究方向。

摘要:

我们对数据驱动系统中的隐私攻击与对策进行了全面分析。我们系统地将针对三个领域的攻击分类:匿名数据(链接攻击和结构化攻击)、统计聚合(重构攻击和差分攻击)以及隐私保护模型(提取、重构、成员推理和逆向攻击)。对于每个类别,我们分析攻击方法、对手能力和脆弱性机制。我们进一步评估了包括扰动技术、随机化方法、查询审计和模型级防御在内的对策,考察其有效性及固有的隐私—效用权衡。我们的分析表明,尽管差分隐私提供了强有力的理论保证,但它面临实现挑战以及对新兴攻击的潜在脆弱性。我们确定了关键研究方向,并为研究人员和从业者提供了一个结构化框架,用于理解日益复杂的数据生态系统中的隐私弹性。

英文摘要:

We present a comprehensive analysis of privacy attacks and countermeasures in data-driven systems. We systematically categorize attacks targeting three domains: anonymous data (linkage and structural attacks), statistical aggregates (reconstruction and differential attacks), and privacy-preserving models (extraction, reconstruction, membership inference, and inversion attacks). For each category, we analyze attack methodologies, adversary capabilities, and vulnerability mechanisms. We further evaluate countermeasures including perturbation techniques, randomization methods, query auditing, and model-level defenses, examining their effectiveness and inherent privacy-utility tradeoffs. Our analysis reveals that while differential privacy offers strong theoretical guarantees, it faces implementation challenges and potential vulnerabilities to emerging attacks. We identify critical research directions and provide researchers and practitioners with a structured framework for understanding privacy resilience in increasingly complex data ecosystems.

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。

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