Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

Husnoo, Muhammad Akbar; Anwar, Adnan; Chakrabortty, Ripon K.; Doss, Robin; Ryan, Mike

doi:10.1109/access.2021.3124309

Cited by 33 publications

(14 citation statements)

References 298 publications

(314 reference statements)

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“…Significant amount of risk is also associated with the privacy concern of the information being shared. Therefore, privacy attacks have gained significant attention [6]. Privacy attacks can reveal the identity and user behavior of the consumers.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

Asre

Anwar

2022

Electronics

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Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be revealed from the actual data. In this research, we have addressed these challenges by using generative adversarial networks for generating energy consumption profile. We have successfully generated synthetic data which is similar to the real energy consumption data. On the basis of the recent research conducted on TimeGAN, we have implemented a framework for synthetic energy consumption data generation that could be useful in research, data analysis and create business solutions. The framework is implemented using the real-world energy dataset, consisting of energy consumption data of the year 2020 for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. The results of implementation is evaluated using various performance measures and the results are showcased using visualizations along with Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, experimental results show that Synthetic data generated using the proposed implementation possess very similar characteristics to the real dataset with high comparison accuracy.

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Section: Introductionmentioning

confidence: 99%

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

Asre

Anwar

2022

Electronics

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“…The centralized aggregation of energy consumption data faces a two main challenges. There are considerable challenges of privacy and security [4], [5] concerning such data due to sense and correlate granular data. The energy consumption data is granular enough such that one can extract individual customer's behaviour.…”

Section: Introductionmentioning

confidence: 99%

FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers

Husnoo¹,

Anwar²,

Hosseinzadeh³

et al. 2022

Preprint

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As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development. Specifically, we note that existing energy load forecasting is centralized, which are not scalable and most importantly, vulnerable to data privacy threats. Besides, REPs are individual market participants and liable to ensure the privacy of their own customers. To address this issue, we propose a novel horizontal privacy-preserving federated learning framework for REPs energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security and scalability. For forecasting, we use a state-of-theart Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations and promises of higher accuracy with time-series data while solving the vanishing gradient problem. Finally, we conduct extensive data-driven experiments using a real energy consumption dataset. Experimental results demonstrate that our proposed federated learning framework can achieve sufficient performance in terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a centralized approach while preserving privacy and improving scalability.

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“…Such a feature leads to a provable privacy guarantee with a quantitative privacy measurement called privacy budget [17], making DP the de-facto standard for privacy preservation in data analysis both in academia and industry [18,19]. However, though DP has observed its prosperity in a wide range of applications [20][21][22][23][24], its integration into image data is understood in a limited way. Particularly, in the context of images, differential privacy has not gained the conceptional privacy where individual contribution is indistinguishable in a database.…”

Section: Introductionmentioning

confidence: 99%

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

Zhang¹,

Ma²,

Cao³

et al. 2022

Preprint

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Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in databases. Nevertheless, there lacks a clear and convincing contextualization of DP in the image database, where individual images' indistinguishable contribution to a specific analysis can be achieved and observed when DP is exerted. As a result, the privacy-accuracy trade-off due to integrating DP is insufficiently demonstrated in the context of differentiallyprivate image databases. This work aims at contextualizing DP in an image database by an explicit and intuitive demonstration of integrating conceptional differential privacy with images. To this end, we design a lightweight approach dedicated to privatizing the image database as a whole and preserving the statistical semantics of the image database to an adjustable level, while making individual images' contribution to such statistics indistinguishable. The designed approach leverages principal component analysis (PCA) to reduce the raw image with a large number of attributes to a lower-dimensional space, whereby DP is performed to decrease the DP load of calculating sensitivity attribute-by-attribute. The DP-exerted image data, which is not visible in its privatized format, is visualized through PCA inverse such that both a human and machine inspector can evaluate the privatization and quantify the privacy-accuracy trade-off in an analysis on the privatized image database. Using the devised approach, we demonstrate the contextualization of DP in images by two use cases based on deep learning models, where we show the indistinguishability of individual images induced by DP and the privatized images' retention of statistical semantics in deep learning tasks, which is elaborated by quantitative analyses on the privacy-accuracy trade-off under different privatization settings.

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Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

Cited by 33 publications

References 298 publications

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

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