Privacy Preserving Time-Series Forecasting of User Health Data Streams

Imtiaz, Sana; Horchidan, Sonia-Florina; Abbas, Zainab; Arsalan, Muhammad; Chaudhry, Hassan Nazeer; Vlassov, Vladimir

doi:10.1109/bigdata50022.2020.9378186

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…However, even ML models trained with raw data can also indirectly reveal sensitive information [17,50,16,49], in particular, RNNs [58]. To protect ML models against such threats, under the state-ofthe-art DP guarantee [22,23], there exist some privacypreserving ML alternatives adopted in the literature, e.g., input [19,31,24,29,10,9], gradient [4,33,51,60,48], and objective perturbation [18].…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…Besides, the work in [30] surveys non-private deep learning applications to mobility datasets in general. Concerning differentially private deep learning, one can find the application of gradient perturbation-based DL models for load forecasting [51], an evaluation of differentially private DL models in fed- erated learning for health stream forecasting [29], the proposal of locally differentially private DL architectures [31], practical libraries for differentially private DL [33,60], and theoretical research works [4,48,19].…”

Section: Discussion and Related Workmentioning

confidence: 99%

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Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

Arcolezi

Couchot

Renaud

et al. 2022

Neural Comput & Applic

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This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered gradient perturbation, which uses the differentially private stochastic gradient descent algorithm to guarantee the privacy of each time series sample in the learning stage. On the other hand, we considered input perturbation, which adds differential privacy guarantees in each sample of the series before applying any learning. We compared four state-of-the-art recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and their Bidirectional architectures, i.e., Bidirectional-LSTM and Bidirectional-GRU. Extensive experiments were conducted with a real-world multivariate mobility dataset, which we published openly along with this paper. As shown in the results, differentially private deep learning models trained under gradient or input perturbation achieve nearly the same performance as non-private deep learning models, with loss in performance varying between 0.57% to 2.8%. The contribution of this Héber H.

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Section: Discussion and Related Workmentioning

confidence: 99%

Section: Discussion and Related Workmentioning

confidence: 99%

Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

Arcolezi

Couchot

Renaud

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…DP provides the required privacy guarantees at the cost of reducing the prediction accuracy of the machine learning model. An end-to-end pipeline consisting of FL and DR is proposed for health data streams using a clustering mechanism to reduce model training time with high accuracy [31]. DP mechanism is used by the system to provide privacy guarantees and results showed that prediction accuracy only decreases by 2% for the trained model due to this change.…”

Section: Differential Privacymentioning

confidence: 99%

“…The differential privacy approach provides an effective mechanism to safeguard the exchange of ML model parameters from clients to FL or edge server [27][28][29]31]. DP also poses all relevant characteristics to mitigate all risks associated with information leakage during model parameter exchange [26,30,32].…”

Section: Differential Privacymentioning

confidence: 99%

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Gu,

Sabrina,

Fan

et al. 2023

IJERPH

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Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client’s data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.

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“…While social media might not represent everyone completely, it provides a sample space of people's opinions. Also, some people might not want to reveal their views due to privacy issues, so even if they are on social media, they might not express their true opinion [18]. Nevertheless, despite all these limitations, social media sentiment analysis provides the nearest approximation of public sentiment.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

et al. 2021

Self Cite

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U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election’s results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets’ API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.

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Privacy Preserving Time-Series Forecasting of User Health Data Streams

Cited by 10 publications

References 20 publications

Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

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