Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

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

doi:10.1109/iotdi.2018.00025

Cited by 50 publications

(40 citation statements)

References 35 publications

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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%

Protecting Sensory Data against Sensitive Inferences

Malekzadeh

Clegg

Cavallaro

et al. 2018

Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems

Self Cite

116

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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 objective is to move from the current binary setting of granting or not permission to an application, toward a model that allows users to grant each application permission over a limited range of inferences according to the provided services. The internal structure of each component of the proposed architecture can be flexibly changed and the trade-off between privacy and utility can be negotiated between the constraints of the user and the underlying application. We validated the proposed architecture in an activity recognition application using two real-world datasets, with the objective of recognizing an activity without disclosing gender as an example of private information. Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50%, the target random guess, from more than 90% when using raw sensor data. We also present and distribute MotionSense, a new dataset for activity and attribute recognition collected from motion sensors.

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Section: Introductionmentioning

confidence: 99%

Protecting Sensory Data against Sensitive Inferences

Malekzadeh

Clegg

Cavallaro

et al. 2018

Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems

Self Cite

116

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“…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%

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

Osia

Shamsabadi

Sajadmanesh

et al. 2020

IEEE Internet Things J.

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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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“…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%

Mobile sensor data anonymization

Malekzadeh

Clegg

Cavallaro

et al. 2019

Proceedings of the International Conference on Internet of Things Design and Implementation

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133

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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 monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.

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Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

Cited by 50 publications

References 35 publications

Protecting Sensory Data against Sensitive Inferences

Protecting Sensory Data against Sensitive Inferences

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

Mobile sensor data anonymization

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