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As Internet-of-Things (IoT) devices rapidly gain popularity, they raise significant privacy concerns given the breadth of sensitive data they can capture. These concerns are amplified by the fact that in many situations, IoT devices collect data about people other than their owner or administrator, and these stakeholders have no say in how that data is managed, used, or shared. To address this, we propose a new model of ownership, IoT Ephemeral Ownership (TEO). TEO allows stakeholders to quickly register with an IoT device for a limited period, and thus claim co-ownership over the sensitive data that the device generates. Device admins retain the ability to decide who may become an ephemeral owner, but no longer have access or control to the private data generated by the device. The encrypted data in TEO is accessible only by entities after seeking explicit permission from the different co-owners of that data. We verify the key security properties of our protocol underpinning TEO in the symbolic model using ProVerif. We also implement a cross-platform prototype of TEO for mobile phones and embedded devices, and integrate it into three real-world application case studies. Our evaluation shows that the latency and battery impact of TEO is typically small, adding ≤187 ms onto one-time operations, and introducing limited (<25%) overhead on recurring operations like private data storage. 

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users’ data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms’ generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms. The GLOBEM website can be found at the-globem.github.io Our datasets are available at physionet.org/content/globem Our codebase is open-sourced at github.com/UW-EXP/GLOBEM