On the feasibility of user de-anonymization from shared mobile sensor data

Underpinning many recent advances in sensing applications (e.g., mHealth) is the ability to safely collect and share mobile sensor data. Research has shown that even from seemingly harmless sensors (e.g., accelerometers, gyroscopes, or magnetometers) an ever expanding set of potentially sensitive user behavior can be inferred. Providing robust anonymity assurances is a principal mechanism for protecting users when data is shared (e.g., with medical professionals or friends). In this paper, we study the feasibility of user de-anonymization from mobile sensor datasets routinely collected on commodity devices (e.g., smartphones). We perform a systematic investigation to quantify the threat of de-anonymization using existing sparsity-based techniques adapted to exploit mobile sensor data characteristics. This preliminary study indicates significant threats to user anonymity exist within shared mobile sensor data and further investigation is warranted.