3 research outputs found

    Maintaining privacy during continuous motion sensing

    Get PDF
    Mobile devices contain sensors which allow continuous recording of a user's motion allowing the development of activity, fitness and health applications. With varied applications, the motion sensors present new privacy problems which require protection. This dissertation builds on previous work with activity and fitness machine learning techniques demonstrating the ability to predict medical values from motion data using smartphones. We conduct two clinical trials collecting a data set of eighty-eight patients and forty-five hours of monitoring to analyze the privacy implications of releasing motion data. We extract a comprehensive set of statistical features from all available smartphone sensors and evaluate feature selection techniques and machine learning models. We find we can predict user identity, phone identity, speed, FEV1/FVC, and activity from the motion signal. Designing a privacy protection mechanism for motion data requires a precise understanding of how the signal predicts the sensitive information. We develop algorithms to conduct private feature selection which identifies features useful for prediction. We find that simply blocking all private features significantly reduces the usefulness of the signal for other predictions. We develop a sensitivity estimation framework to calibrate the noise for each private feature requiring an order of magnitude less noise than differential privacy sensitivity. We find adding noise to private features calibrated using the sensitivity estimate is effective at reducing the prediction of five tested target predictions. Our methods hide both user and phone identification while allowing other prediction but cannot hide activity, FEV1/FVC and speed without significantly lowering the accuracy of other predictions. Our methods are still effective when the attacker has prior knowledge of the noise distribution. The methods presented in this dissertation demonstrate the need for privacy in motion data and provide a framework for protecting sensitive user information in motion readings

    Assembling Sessions

    No full text
    International audienceSessions are a central paradigm inWeb services to implement decentralized transactions with multiple participants. Sessions enable the cooperation of workflows while at the same time avoiding the mixing of workflows from distinct transactions. Languages such as BPEL, ORC, AXML that implement Web Services usually realize sessions by attaching unique identifiers to transactions. The expressive power of these languages makes the properties of the implemented services undecidable. In this paper, we propose a new formalism for modelling web services. Our model is session-based, but avoids using session identifiers. The model can be translated to a dialect of Petri nets that allows the verification of important properties of web services
    corecore