2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI) 2018
DOI: 10.1109/iotdi.2018.00025
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Abstract: An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches.In this paper, we propos… Show more

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Cited by 47 publications
(36 citation statements)
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“…Unlike privacy-preserving works that only hide users' identity by sharing population data using generative models for data synthesis [2,9], our solution concerns sensitive information included in a single user's data. There are, however, some methods which transform only selected temporal sections of sensor data that correspond to predefined sensitive activities [11,12], our framework enables concurrently eliminating private information from each section of data, while keeping the utility of shared data.…”
Section: Introductionmentioning
confidence: 99%
“…Unlike privacy-preserving works that only hide users' identity by sharing population data using generative models for data synthesis [2,9], our solution concerns sensitive information included in a single user's data. There are, however, some methods which transform only selected temporal sections of sensor data that correspond to predefined sensitive activities [11,12], our framework enables concurrently eliminating private information from each section of data, while keeping the utility of shared data.…”
Section: Introductionmentioning
confidence: 99%
“…The first approach to increase the privacy of the intermediate feature is to reduce its dimensionality using Principle Component Analysis (PCA) or autoencoder-based solutions [11], as these methods try to preserve the primary structure of the input signal as much as possible, and remove all the other unnecessary details. This mechanism also reduces the communication overhead between the user and the cloud.…”
Section: Dimensionality Reductionmentioning
confidence: 99%
“…In this way, a well-trained autoencoder captures prominent and desired patterns in the data and ignores noise or undesired patterns [35]. Moreover, a latent representation can be learned that removes some meaningful patterns from the data to reduce the risk of inferring sensitive information [21].…”
Section: Related Workmentioning
confidence: 99%