Securing Machine Learning Engines in IoT Applications with Attribute-Based Encryption

Agus Kurniawan, M. Kyas
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引用次数: 3

Abstract

Machine learning has been adopted widely to perform prediction and classification. Implementing machine learning increases security risks when computation process involves sensitive data on training and testing computations. We present a proposed system to protect machine learning engines in IoT environment without modifying internal machine learning architecture. Our proposed system is designed for passwordless and eliminated the third-party in executing machine learning transactions. To evaluate our a proposed system, we conduct experimental with machine learning transactions on IoT board and measure computation time each transaction. The experimental results show that our proposed system can address security issues on machine learning computation with low time consumption.
使用基于属性的加密保护物联网应用中的机器学习引擎
机器学习已被广泛应用于预测和分类。当计算过程涉及训练和测试计算的敏感数据时,实现机器学习会增加安全风险。我们提出了一个在不修改内部机器学习架构的情况下保护物联网环境中的机器学习引擎的系统。我们提出的系统设计为无密码,消除了第三方执行机器学习事务。为了评估我们提出的系统,我们在IoT板上进行了机器学习交易的实验,并测量了每个交易的计算时间。实验结果表明,该系统能够以较低的时间消耗解决机器学习计算中的安全问题。
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