QueryCheetah：针对基于查询系统的属性推断攻击的快速自动化发现
QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems
AI总结：
本文提出QueryCheetah方法，用于快速自动化发现针对基于查询系统的属性推断攻击，在发现更强攻击的同时比现有最先进方法快18倍，并支持全面隐私风险评估及发现绕过临时防御的攻击。
摘要：
基于查询的系统（QBSs）是共享数据的关键方法之一。QBSs允许分析师从受保护的私有数据集中请求聚合信息。攻击是确保QBSs真正保护隐私的关键组成部分。然而，攻击的开发和测试非常耗费人力，并且无法应对系统日益增长的复杂性。自动化方法已被证明具有前景，但目前计算开销极大，限制了其在实际中的应用。我们在此提出QueryCheetah，一种快速且有效的方法，用于自动发现针对QBSs的隐私攻击。我们将QueryCheetah实例化用于属性推断攻击，并表明它能够发现比先前方法更强的攻击，同时比最先进的自动化方法快18倍。随后，我们展示了QueryCheetah如何使系统开发者能够全面评估隐私风险，包括针对不同攻击者能力和目标个体的情况。最后，我们展示了QueryCheetah可以开箱即用地用于在更大语法空间中寻找攻击，以及发现针对临时防御措施的绕过方法。
英文摘要：
Query-based systems (QBSs) are one of the key approaches for sharing data. QBSs allow analysts to request aggregate information from a private protected dataset. Attacks are a crucial part of ensuring QBSs are truly privacy-preserving. The development and testing of attacks is however very labor-intensive and unable to cope with the increasing complexity of systems. Automated approaches have been shown to be promising but are currently extremely computationally intensive, limiting their applicability in practice. We here propose QueryCheetah, a fast and effective method for automated discovery of privacy attacks against QBSs. We instantiate QueryCheetah on attribute inference attacks and show it to discover stronger attacks than previous methods while being 18 times faster than the state-of-the-art automated approach. We then show how QueryCheetah allows system developers to thoroughly evaluate the privacy risk, including for various attacker strengths and target individuals. We finally show how QueryCheetah can be used out-of-the-box to find attacks in larger syntaxes and workarounds around ad-hoc defenses.
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。
