A statistical hypothesis test determines whether a hypothesis should be rejected based on samples from populations. In particular, randomized controlled experiments (or A/B testing) that compare population means using, e.g., t-tests, have been widely deployed in technology companies to aid in making data-driven decisions. Samples used in these tests are collected from users and may contain sensitive information. Both the data collection and the testing process may compromise individuals’ privacy. In this paper, we study how to conduct hypothesis tests to compare population means while preserving privacy. We use the notation of local differential privacy (LDP), which has recently emerged as the main tool to ensure each individual’s privacy without the need of a trusted data collector. We propose LDP tests that inject noise into every user’s data in the samples before collecting them (so users do not need to trust the data collector), and draw conclusions with bounded type-I (significance level) and type-II errors (1 - power). Our approaches can be extended to the scenario where some users require LDP while some are willing to provide exact data. We report experimental results on real-world datasets to verify the effectiveness of our approaches.

Artificial intelligence gradually finds its wider applications in mobile phones. For a better user experience, sensing users' activity or context accurately is important to enable intelligent mobile services. In this poster, we present a Convolutional Neural Network (CNN) model to detect a user's current mode of transport. Our model utilizes mobile sensor data such as accelerometer and gyroscope in the spectral domain as inputs in order to mitigate mobile phone placement and orientation factors. Encouraging experimental results show that the proposed scheme solves efficiently the problem of pose and orientation change in the transportation mode detection. In addition, our CNN model has a simplified structure, suitable for running on a mobile device with existing neural processing units (NPU) hardware capability.