Individual privacy constraints on time-series data

Laforet, Fabian; Buchmann, Erik; Böhm, Klemens

doi:10.1016/j.is.2015.06.006

Cited by 17 publications

(6 citation statements)

References 30 publications

(51 reference statements)

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“…Meter data aggregation (across the time horizon and/or across different meters) is one of the popular queries that the utility company can mainly use for customer billing, creating strategies for mitigating peak demand, and/or creating cost-effective plans for balancing the supply and demand. Other queries included 1) perturbating individual timeseries metered profile [54][55][56][57][58], 2) spectral analysis [59,60], and 3) state estimation in distribution network [61].…”

Section: Local (Distributed) and Non-interactive Modelsmentioning

confidence: 99%

A review of preserving privacy in data collected from buildings with differential privacy

Kim

H²,

Hong

et al. 2022

Journal of Building Engineering

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Section: Local (Distributed) and Non-interactive Modelsmentioning

confidence: 99%

A review of preserving privacy in data collected from buildings with differential privacy

Kim

H²,

Hong

et al. 2022

Journal of Building Engineering

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“…Reference Local GesAct Identity Sensor Unseen Perturbations [17,18,19] Synthesis [22,24,25] Filtering [26,30,33] [27]…”

Section: Mechanismmentioning

confidence: 99%

“…Adversarial learning enables one to approximate an underlying distribution to generate new data that are similar to the existing ones [20,21]. To provide a privacy guarantee, generators can be trained under the constraint of differential privacy [22,23] or with constraints on the type of information that should be unsynthesized in the data [24]. However, these mechanisms are used for offline dataset publishing by a data aggregator [25], not for online data transformation at the user side.…”

Section: Filtering and Transformations Oursmentioning

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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“…In fact, feature sharing can be considered as a mapping mechanism to control and reduce the inferences possible from shared sensory data [21]. Laforet et al [22] introduced an optimization method to generate and publish an entirely new time-series from the original ones by considering some predefined constraints on the type of information that can be inferred. Another solution would be to publish just task-relevant high-level features.…”

Section: Mappingmentioning

confidence: 99%

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

Malekzadeh

Clegg

Haddadi

2018

2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)

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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 propose a privacy-preserving sensing framework for managing access to time-series data in order to provide utility while protecting individuals' privacy. We introduce Replacement AutoEncoder, a novel algorithm which learns how to transform discriminative features of data that correspond to sensitive inferences, into some features that have been more observed in non-sensitive inferences, to protect users' privacy. This efficiency is achieved by defining a user-customized objective function for deep autoencoders. Our replacement method will not only eliminate the possibility of recognizing sensitive inferences, it also eliminates the possibility of detecting the occurrence of them. That is the main weakness of other approaches such as filtering or randomization. We evaluate the efficacy of the algorithm with an activity recognition task in a multi-sensing environment using extensive experiments on three benchmark datasets. We show that it can retain the recognition accuracy of state-of-the-art techniques while simultaneously preserving the privacy of sensitive information. Finally, we utilize the GANs for detecting the occurrence of replacement, after releasing data, and show that this can be done only if the adversarial network is trained on the users' original data.

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Individual privacy constraints on time-series data

Cited by 17 publications

References 30 publications

A review of preserving privacy in data collected from buildings with differential privacy

A review of preserving privacy in data collected from buildings with differential privacy

Privacy and utility preserving sensor-data transformations

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

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