Collective Protection: Preventing Sensitive Inferences via Integrative Transformation Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic information that can be inferred from the time series sensor data. Existing sensitive information protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive information for training. Considering this gap, we propose a novel user sensitive information protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive information. The presented approach transforms raw sensor data into a new format that has a style (sensitive information) of random noise and a content (desired information) of the raw sensor data, thus is free of user sensitive information for training and able to collectively protect all sensitive information at once. Our implementation and experiments on two realworld multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the protection for all sensitive information at once without requiring the knowledge of usersu0027 personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.

Prying into Private Spaces Using Mobile Device Motion Sensors Human made structures are designed in a predictable manner, conforming to the expectations of those who use them. These underlying patterns lend themselves to repetition in the way people get between different locations. We investigated the feasibility of an attacker using motion sensor data against their target, with the objective of predicting where they move and what activities they engaged in. In this work, we show that the gyroscope and accelerometer can be used to drive a privacy attack that stealthily maps out a useru0027s private space with high accuracy. In particular, we show that a mobile app with access to this data can leverage it to analyze a useru0027s step execution dynamics, turn operations, and general body movement activities and then methodically combine this information to map out paths and landmarks in protected spaces, such as houses. Using a dataset of 26 users who executed a number of activities and a combination of classification, regression, and distance matching techniques, we show this privacy attack to generate maps whose Normalized Hausdorff Distance from the groundtruth is as low as 0.1159.