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Open Access Publications from the University of California

Personal Data Vault: A Privacy Architecture for Mobile Personal Sensing


Participatory sensing tasks deployed mobile devices to form interactive, participatory sensor networks that enable public and professional users to gather, analyze and share local knowledge. Mobile Personal Sensing (MPS) is a platform for participatory sensing with which users use mobile phones to record and transmit sound, images, location, motion data, and web services to aggregate and interpret the assembled information. The data gathered through MPS is personal, as well as being potentially valuable in many aspects; it quantifies habits, routines, associations, and is easy to mine. However, for these reasons, protecting individual privacy, documenting ownership, and providing visibility of processing are important. We propose Personal Data Vault (PDV), the architecture to support these new design criteria by “auditing” all activities on the data (TraceAudit) and dynamically “re-sampling” data feeds to service providers (Adaptive Filter). The TraceAudit allows the user to track how the data is processed as well as who is using the data in order to provide transparency of data processing and foster a market of “certified” service providers. The adaptive filters govern how the data is sent from PDV to service providers in order to provide a better quality of services with minimal data using two methods: error-tolerant data sampling and anomaly detection.

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