DEEProtect：在移动传感应用中实现基于推理的访问控制
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications
摘要：
个人感觉数据被上下文感知移动应用程序用来提供实用性。然而，同样的数据也可以被对手用来对用户做出敏感的推断，从而侵犯了她的隐私。我们提出了DEEProtect，一个框架，使一种新形式的推理控制，其中移动应用程序与传感器数据的访问是有限的（可证明）在他们的能力，使用户的敏感数据和行为的推断。DEEProtect采用两层隐私策略。首先，它利用新颖的自动编码器技术来执行数据最小化，并限制共享的信息量;学习网络用于导出传感器数据的紧凑表示，该表示仅由与授权的实用程序提供的推断相关的特征组成。其次，DEEProtect混淆了以前学习的特征，从而提供了一个额外的保护层，以防止敏感的推断。我们的框架既支持传统的，以及一个新的放松的本地差分隐私，提高效用的概念。通过理论分析和大量的实验，使用真实世界的数据集，我们证明，与现有的方法相比，DEEProtect提供了可证明的隐私保证，实用性提高了8倍。最后，DEEProtect共享从扰动特征重建的模糊但原始的传感器数据，因此不需要更改现有的应用程序界面。
英文摘要：
Personal sensory data is used by context-aware mobile applications to provide utility. However, the same data can also be used by an adversary to make sensitive inferences about a user thereby violating her privacy. We present DEEProtect, a framework that enables a novel form of inference control, in which mobile apps with access to sensor data are limited (provably) in their ability to make inferences about user's sensitive data and behavior. DEEProtect adopts a two-layered privacy strategy. First, it leverages novel autoencoder techniques to perform data minimization and limits the amount of information being shared; the learning network is used to derive a compact representation of sensor data consisting only of features relevant to authorized utility-providing inferences. Second, DEEProtect obfuscates the previously learnt features, thereby providing an additional layer of protection against sensitive inferences. Our framework supports both conventional as well as a novel relaxed notion of local differential privacy that enhances utility. Through theoretical analysis and extensive experiments using real-world datasets, we demonstrate that when compared to existing approaches DEEProtect provides provable privacy guarantees with up to 8x improvement in utility. Finally, DEEProtect shares obfuscated but raw sensor data reconstructed from the perturbed features, thus requiring no changes to the existing app interfaces.
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。
