Proceedings of the International Conference on Internet of Things Design and Implementation 2019
DOI: 10.1145/3302505.3310068
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Mobile sensor data anonymization

Abstract: Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as m… Show more

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Cited by 124 publications
(79 citation statements)
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References 32 publications
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“…The fifth dataset [34] was used to examine a new approach to a multi-objective loss function for training deep autoencoders for HAR applications. The dataset was recorded by 24 people (10 women and 14 men) using a smartphone in their pocket.…”
Section: Datasetsmentioning
confidence: 99%
“…The fifth dataset [34] was used to examine a new approach to a multi-objective loss function for training deep autoencoders for HAR applications. The dataset was recorded by 24 people (10 women and 14 men) using a smartphone in their pocket.…”
Section: Datasetsmentioning
confidence: 99%
“…The goal of training AAE is to minimize the privacy loss by minimizing the amount of information leakage from U to X . Hence, we use adversarial training to approximate the mutual information by estimating the posterior distribution of the sensitive data given the released data [38].…”
Section: Anonymization Autoencodermentioning
confidence: 99%
“…To simplify the process of encoding data into a lower-dimensional representation and then decoding it to the original dimension with convolutional filters, we set W to be a power of 2. The larger W, the lower the possibility of taking advantage of the correlation among the successive windows by adversaries [38]. But larger window sizes increase the delay for real-time apps.…”
Section: Anonymizationmentioning
confidence: 99%
“…Çalışmamızda kullanılan veriler daha önce kontrollü bir şekilde kaydedilip sonra konu ile ilgili akademik çalışmaların yapıldığı test edilmiş güvenilir bir veridir [1]. 3 eksen ivme ölçer verisi kullanılmıştır.…”
Section: Materyal Ve Yöntemunclassified
“…Çalışmamızda ivme ölçer sensörü kaydına ilişkin veriler kullanılmıştır. Veriler M. Malekzadeh ve arkadaşlarının [1] yaptığı çalışma neticesinde çeşitli boy, kilo, yaş ve cinsiyet bilgisine sahip 24 kişiden alınmıştır. Çalışmamızda ise 5 erkek ve 5 kadın bireye ait ivme ölçer verileri kullanılmıştır.…”
Section: Introductionunclassified