Oblivious Sensor Fusion via Secure Multi-Party Combinatorial Filter Evaluation
Oblivious Sensor Fusion via Secure Multi-Party Combinatorial Filter Evaluation
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DOI：
10.1109/cdc45484.2021.9683202
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
William E. Curran;Cesar A. Rojas;Leonardo Bobadilla;Dylan A. Shell
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文献类型：
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作者：
William E. Curran;Cesar A. Rojas;Leonardo Bobadilla;Dylan A. Shell
给定分布在整个环境中的传感器单元，当传感器希望限制其特定读数的知识时，我们考虑将读数合并到单个连贯的视图中的问题。标准的融合方法不能保证好奇的参与者可能会学到什么。对于需要隐私保证的应用程序，我们引入了一种融合方法，限制了可以推断的内容。首先，它形成一个不受底层传感器数据影响的聚合流，然后在组合过滤器上对该流进行评估。这是通过建立在密码原语之上的安全多方计算技术来实现的，我们将其扩展并应用于离散传感器信号的融合问题。我们证明了这些扩展在半诚实对手模型下是安全的。此外，对于一个简单的目标跟踪案例研究，我们检查了一个概念验证实现：分析体系结构中组件的(经验)运行时间，并提出未来改进的方向。
Given sensor units distributed throughout an environment, we consider the problem of consolidating readings into a single coherent view when sensors wish to limit knowledge of their specific readings. Standard fusion methods make no guarantees about what curious participants may learn. For applications where privacy guarantees are required, we introduce a fusion approach that limits what can be inferred. First, it forms an aggregate stream, oblivious to the underlying sensor data, and then evaluates that stream on a combinatorial filter. This is achieved via secure multi-party computation techniques built on cryptographic primitives, which we extend and apply to the problem of fusing discrete sensor signals. We prove that the extensions preserve security under the model of semi-honest adversaries. Also, for a simple target tracking case study, we examine a proof-of-concept implementation: analyzing the (empirical) running times for components in the architecture and suggesting directions for future improvement.