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The QoS and Privacy Trade-off of Adversarial Deep Learning: An Evolutionary Game Approach
Computers & Security ( IF 4.8 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.cose.2020.101876
Zhe Sun , Lihua Yin , Chao Li , Weizhe Zhang , Ang Li , Zhihong Tian

Abstract Deep learning-based service has received great success in many fields and changed our daily lives profoundly. To support such service, the provider needs to continually collect data from users and protect users’ privacy at the same time. Adversarial deep learning is of widespread interest to service providers because of its ability to automatically select privacy-preserving features that have less impact on the Quality of Service (QoS). However, choosing an appropriate threshold to adjust the weight of the QoS and privacy-preserving becomes a significant issue for both the provider and users. In this paper, we model the contradicting incentives between the QoS and privacy-preserving as an evolutionary game, and achieve an Evolutionary Stable Strategy (ESS) to help users decide whether to submit high-quality data or not. First, we define the individual contribution to the QoS and the privacy cost of submitting high-quality data. Then, we propose an incentive mechanism to deal with the problems that the users are bounded rational and do not own the complete knowledge about other users’ choices. Moreover, we propose an ESS-based algorithm of balancing the QoS and privacy risk, which reaches a stable state of maintaining long-term service by multiple iterations. Finally, we conduct the simulation experiments to demonstrate that our strategy can efficiently incentivize users to make a trade-off between the long-term benefits of the QoS and the current cost of privacy.

中文翻译:

对抗性深度学习的 QoS 和隐私权衡:一种进化博弈方法

摘要 基于深度学习的服务在许多领域都取得了巨大的成功,深刻地改变了我们的日常生活。为了支持这样的服务,提供商需要不断地从用户那里收集数据,同时保护用户的隐私。对抗性深度学习受到服务提供商的广泛关注,因为它能够自动选择对服务质量 (QoS) 影响较小的隐私保护功能。然而,选择一个合适的阈值来调整 QoS 和隐私保护的权重对于提供商和用户来说都是一个重要的问题。在本文中,我们将 QoS 和隐私保护之间的矛盾激励建模为一个进化博弈,并实现了一个进化稳定策略(ESS)来帮助用户决定是否提交高质量的数据。第一的,我们定义了个人对 QoS 的贡献和提交高质量数据的隐私成本。然后,我们提出了一种激励机制来处理用户是有限理性的并且不拥有其他用户选择的完整知识的问题。此外,我们提出了一种基于 ESS 的平衡 QoS 和隐私风险的算法,通过多次迭代达到维持长期服务的稳定状态。最后,我们进行了模拟实验,以证明我们的策略可以有效地激励用户在 QoS 的长期收益和当前隐私成本之间进行权衡。我们提出了一种激励机制来处理用户是有限理性的并且不拥有其他用户选择的完整知识的问题。此外,我们提出了一种基于 ESS 的平衡 QoS 和隐私风险的算法,通过多次迭代达到维持长期服务的稳定状态。最后,我们进行了模拟实验,以证明我们的策略可以有效地激励用户在 QoS 的长期收益和当前隐私成本之间进行权衡。我们提出了一种激励机制来处理用户是有限理性的并且不拥有其他用户选择的完整知识的问题。此外,我们提出了一种基于 ESS 的平衡 QoS 和隐私风险的算法,通过多次迭代达到维持长期服务的稳定状态。最后,我们进行了模拟实验,以证明我们的策略可以有效地激励用户在 QoS 的长期收益和当前隐私成本之间进行权衡。
更新日期:2020-09-01
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