Protecting Sensory Data against Sensitive Inferences

Malekzadeh, Mohammad; Clegg, R. E. S.; Cavallaro, Andrea; Haddadi, Hamed

doi:10.1145/3195258.3195260

Cited by 122 publications

(102 citation statements)

References 19 publications

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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%

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed

Özçelebi

Lukkien

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

207

199

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Deep learning methods are successfully used in applications pertaining to ubiquitous computing, pervasive intelligence, health, and well-being. Speci cally, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural networks, thanks to their ability to learn semantic representations directly from raw input. However, in order to extract generalizable features massive amounts of well-curated data are required, which is a notoriously challenging task; hindered by privacy issues and annotation costs. erefore, unsupervised representation learning (i.e., learning without manually labeling the instances) is of prime importance to leverage the vast amount of unlabeled data produced by smart devices. In this work, we propose a novel self-supervised technique for feature learning from sensory data that does not require access to any form of semantic labels, i.e., activity classes. We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal. By exploiting these transformations, we demonstrate that simple auxiliary tasks of the binary classi cation result in a strong supervisory signal for extracting useful features for the down-stream task. We extensively evaluate the proposed approach on several publicly available datasets for smartphone-based HAR in unsupervised, semi-supervised and transfer learning se ings. Our method achieves performance levels superior to or comparable with fully-supervised networks trained directly with activity labels, and it performs signi cantly be er than unsupervised learning through autoencoders. Notably, for the semi-supervised case, the self-supervised features substantially boost the detection rate by a aining a kappa score between 0.7 − 0.8 with only 10 labeled examples per class. We get similar impressive performance even if the features are transferred from a di erent data source. Self-supervision drastically reduces the requirement of labeled activity data, e ectively narrowing the gap between supervised and unsupervised techniques for learning meaningful representations. While this paper focuses on HAR as the application domain, the proposed approach is general and could be applied to a wide variety of problems in other areas. 000:2 • A. Saeed et al.activity recognition (HAR), 1D convolutional and recurrent neural networks trained on raw labeled signals signi cantly improve the detection rate over traditional methods [20,44,68,72,73]. Despite the recent advances in the eld of HAR, learning representations from a massive amount of unlabeled data still presents a signi cant challenge. Obtaining large, well-curated activity recognition datasets is problematic due to a number of issues. First, smartphone data are privacy sensitive, which makes it hard to collect su cient amounts of user-activity instances in a real-life se ing. Second, the annotation cost and the time it takes to generate a large volume of labeled instances are prohibitive. Finally, the diversity of devices, types of embedd...

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Section: 16mentioning

confidence: 99%

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed

Özçelebi

Lukkien

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

207

199

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“…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%

Privacy and utility preserving sensor-data transformations

Malekzadeh

Clegg

Cavallaro

et al. 2020

Pervasive and Mobile Computing

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Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users' devices. These transformations aim at eliminating patterns that can be used for user reidentification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data.

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“…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%

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

Osia

Shamsabadi

Sajadmanesh

et al. 2020

IEEE Internet Things J.

Self Cite

149

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Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces and in our daily lives. These devices often rely on continuous data collection and machine learning models for analytics and actuations. However, this approach introduces a number of privacy and efficiency challenges, as the service operator can perform arbitrary inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. We manipulate the model with Siamese fine-tuning and propose a noise addition mechanism to ensure that the output of the user's device contains no extra information except what is necessary for the main task, preventing any secondary inference on the data. We then evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also asses the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy and performance.

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Protecting Sensory Data against Sensitive Inferences

Cited by 122 publications

References 19 publications

Multi-task Self-Supervised Learning for Human Activity Detection

Multi-task Self-Supervised Learning for Human Activity Detection

Privacy and utility preserving sensor-data transformations

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

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