Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT Devices
Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT Devices
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DOI：
10.1109/jiot.2023.3288523
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发表时间：
2024-01
影响因子：
10.6
通讯作者：
Hossein Khalili;Hao-Jen Chien;Amin Hass;Nader Sehatbakhsh
中科院分区：
文献类型：
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作者：
Hossein Khalili;Hao-Jen Chien;Amin Hass;Nader Sehatbakhsh
近年来，当最终用户在物联网和云节点之间分发了所需的计算，因此目睹了混合物IoT云应用的激增。在达到显着速度的同时，这种方法的主要警告是数据隐私。在过去的几年中，隐私的方法受到了主要关注，主要是因为它们可以解决这个问题。在几个建议中，基于动态编码和扰动的方法提供了灵活性和低开销。但是，他们经常考虑一个弱的对手模型或忽略实际限制，例如编码延迟和复杂性。这项工作提出了一种新的保护隐私方法来解决这些问题。本文的主要贡献是双重的。首先，与最先进的情况不同，它提出了一种基于进化算法的新方法，以系统地评估编码算法对大量潜在对手的鲁棒性。其次，它制定了一种动态的混淆策略，该策略可以在现实的Iot-Cloud混合生态系统和隐私需求中平衡潜伏期需求。此外，我们的方法提供了独特的好处：它可以单独用于隐私保护，也可以与大多数现有方法集成以增强隐私和减少延迟。我们提出的方法的适用性和有效性是在现实世界中云环境中使用两个流行的深神网络进行彻底评估的。我们研究方法对重要指标的影响，例如准确性和隐私。我们的结果表明，我们提出的方法可以平均将给定的云混合生态系统的总体隐私提高超过10％。
Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user distributes the desired computation between the IoT and cloud nodes. While achieving significant speed up, the major caveat of this approach is data privacy. Privacy-preserving methods have received major attention in the past few years, mainly because they can potentially solve this issue. Among several proposals, methods based on dynamic encoding and perturbation offer flexibility and low overhead. However, they often consider a weak adversary model or overlook practical limitations, such as encoding latency and complexity. This work proposes a new privacy-preserving method to address these issues. The key contributions of this article are twofold. First, unlike state-of-the-art, it proposes a new approach based on evolutionary algorithms to systematically evaluate the robustness of the encoding algorithm against a large population of potential adversaries. Second, it develops a dynamic obfuscation strategy that balances latency requirements in a realistic IoT-cloud hybrid ecosystem and privacy demands. Additionally, our method offers a unique benefit: it can be used alone for privacy protection, or it can be integrated with most existing methods to enhance privacy and reduce latency. The applicability and effectiveness of our proposed methods are thoroughly evaluated using two popular deep neural networks in a real-world IoT-cloud setting. We study the impact of our approach on important metrics, such as accuracy and privacy. Our results show that our proposed method can improve the overall privacy of a given IoT-cloud hybrid ecosystem by more than 10% on average.