Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users’ devices. These transformations aim at eliminating patterns that can be used for user re-identification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data.

当来自可穿戴或便携式设备的原始传感器数据与云辅助应用程序共享时，敏感推理和用户重新标识是对隐私的主要威胁。为了缓解这些威胁，我们提出了在与用户设备上运行的应用程序共享传感器数据之前转换传感器数据的机制。这些转换旨在消除可用于用户重新标识或推断潜在敏感活动的模式，同时为目标应用程序（或任务）带来较小的实用程序损失。我们表明，在手势和活动识别任务上，我们可以防止潜在敏感活动的推断，同时将非敏感活动的识别准确度降低到小于5个百分点。