Mitigating Privacy Leak by Injecting Unique Noise into the Traffic of Smart Speakers

In recent years, in the Internet, it is common to encrypt communication lines for the assumption that the contents of communication are eavesdropped, but even if the communication lines are secure, there are many cases in which the possibility of the contents of communication being leaked to a third party by a side-channel attack is not taken into account. Although it is important that the contents of all communication are not known by the third party, the information related to privacy may be leaked unintentionally by only encrypting traffics. In this study, we made smart speakers, an IoT device that has started to penetrate into our daily lives, to perform eight kinds of activities, and used their traffic data to estimate their activities with CNN, and we were able to estimate the activities with 98% accuracy. As a counter measure, we propose a method to reduce the accuracy of estimation by adding dummy packets to their communication traffic as noise. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model. In this study, we made smart speakers, an IoT device that has started to penetrate into our daily lives, to perform eight kinds of activities, and used their traffic data to estimate their activities with CNN, and we were able to estimate the activities with 98% accuracy. As a counter measure, we propose a method to reduce the accuracy of estimation by adding dummy packets to their communication traffic as noise. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model.