Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems 2018
DOI: 10.1145/3195258.3195260
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Protecting Sensory Data against Sensitive Inferences

Abstract: There is growing concern about how personal data are used when users grant applications direct access to the sensors of their mobile devices. In fact, high resolution temporal data generated by motion sensors reflect directly the activities of a user and indirectly physical and demographic attributes. In this paper, we propose a feature learning architecture for mobile devices that provides flexible and negotiable privacy-preserving sensor data transmission by appropriately transforming raw sensor data. The ob… Show more

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Cited by 112 publications
(94 citation statements)
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References 19 publications
(18 reference statements)
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“…MotionSense. e MotionSense dataset [40] comprises an accelerometer, gyroscope, and altitude data from 24 participants of varying age, gender, weight, and height. It was collected using an iPhone6s, which is kept in the user's front pocket.…”
Section: 16mentioning
confidence: 99%
“…MotionSense. e MotionSense dataset [40] comprises an accelerometer, gyroscope, and altitude data from 24 participants of varying age, gender, weight, and height. It was collected using an iPhone6s, which is kept in the user's front pocket.…”
Section: 16mentioning
confidence: 99%
“…To evaluate the AAE, we need a dataset containing several users to show how we can hide users' gender or identity. Therefore, we use MotionSense [43] that contains the collected data of 24 users in a range of gender, age, and height who performed 6 activities. We also evaluate the compound architecture (RAE+AAE) on a case study using the MotionSense dataset.…”
Section: Discussionmentioning
confidence: 99%
“…True-positive rate for each activity and gender classification accuracy (%) using a convolutional neural network for each stage of the compound model on MotionSense[43] dataset.…”
mentioning
confidence: 99%
“…Dataset. We used the MotionSense dataset [24] for all the stages of Siamese fine-tuning, predictive accuracy assessment, and transfer learning. This dataset contains time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate), collected by an iPhone 6s kept in the 24 participant's front pocket in 15 trials doing the following activities: downstairs, upstairs, walking, and jogging.…”
Section: Activity Recognitionmentioning
confidence: 99%