The Transparency of Big Data, Data Harvesting and Digital Twins

Kendzierskyj, Stefan; Jahankhani, Hamid; Jamal, Arshad; Ibarra, Jaime

doi:10.1007/978-3-030-11289-9_6

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…However, FedFast [60] relies on user grouping based upon profile similarity. This similarity is computed by sharing user's personal demographic information with the central server, leaving users vulnerable to identity leakage [45]. Some clustering based FL approaches have been proposed that support users' behavioral heterogeneity without sharing/leaking any user data.…”

Section: Federated Recommender System(frs)mentioning

confidence: 99%

ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences

Imran¹,

Yin²,

Chen³

et al. 2022

Preprint

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Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent research on upscaling and privatizing personalized recommendation services, using on-device data to learn recommender models locally. These models are then aggregated globally to obtain a more performant model, while maintaining data privacy. Typically, federated recommender systems (FRSs) do not take into account the lack of resources and data availability at the enddevices. In addition, they assume that the interaction data between users and items is i.i.d. and stationary across end-devices (i.e., users), and that all local recommender models can be directly averaged without considering the user's behavioral diversity. However, in real scenarios, recommendations have to be made on end-devices with sparse interaction data and limited resources. Furthermore, users' preferences are heterogeneous and they frequently visit new items. This makes their personal preferences highly skewed, and the straightforwardly aggregated model is thus ill-posed for such non-i.i.d. data. In this paper, we propose Resource Efficient Federated Recommender System (ReFRS) to enable decentralized recommendation with dynamic and diversified user preferences. On the device side, ReFRS consists of a lightweight self-supervised local model built upon the variational autoencoder for learning a user's temporal preference from a sequence of interacted items. On the server side, ReFRS utilizes a scalable semantic sampler to adaptively perform model aggregation within each identified cluster of similar users. The clustering module operates in an asynchronous and dynamic manner to support efficient global model update and cope with shifting user interests. As a result, ReFRS achieves superior performance in terms of both accuracy and scalability, as demonstrated by comparative experiments on real datasets.

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Section: Federated Recommender System(frs)mentioning

confidence: 99%

ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences

Imran¹,

Yin²,

Chen³

et al. 2022

Preprint

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show abstract

“…Over time, the three Vs have been extended to five Vs by the addition of 'Value,' the value of the data (often relative to a point in time), and 'Velocity,' the rapid increase in the volume of the data [14]. Big data processing often encounters the limitations of traditional databases, software technologies, and established processing methodologies [15].…”

Section: Literature Backgroundmentioning

confidence: 99%

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

2021

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The paper presents possible approaches for reducing the volume of data generated by simulation optimisation performed with a digital twin created in accordance with the Industry 4.0 concept. The methodology is validated using an application developed for controlling the execution of parallel simulation experiments (using client–server architecture) with the digital twin. The paper describes various pseudo-gradient, stochastic, and metaheuristic methods used for finding the global optimum without performing a complete pruning of the search space. The remote simulation optimisers reduce the volume of generated data by hashing the data. The data are sent to a remote database of simulation experiments for the digital twin for use by other simulation optimisers.

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“…4. Kendzierskyj et al [34] 2019 "The transparency of big data, data harvesting and digital twins." 5.…”

Section: # Authorsmentioning

confidence: 99%

“…Also, since blockchain technology is in some ways equal to the participants (although it is pointed out in the paper that this is actually not always the case), this impression is strengthened. In the paper posi- In the article by Kendzierskyj et al [34] there are true ethical questions relating to internet of mobile things (IoMT) devices. The consequences of how these devices and the applications analysing the data they gather can be very important to large amounts of people, and we do not have clear answers on how to solve the issues they raise.…”

Section: Finjehew 2021;13(3) 200mentioning

confidence: 99%

See 1 more Smart Citation

The state of the art of the blockchain ethics in healthcare: A systematic literature review

Hyrynsalmi

Kimppa

2021

FinJeHeW

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Blockchain is a software innovation which is based on a cryptographically secured, decentralised, and distributed storage of data. The technological breakthrough was done as a part of and became familiar through cryptocurrencies, where it is used to openly store currency transactions among its users. Blockchain technology has been since proposed and used in various domains ranging from open contracts to electronic voting—as well as in various purposes in eHealth, medical and well-being applications. However, its usage in these sectors possesses several ethical questions as these environments are full of personal and private patient information. To study the state-of-the-art of the blockchain ethics in healthcare, this study presents a systematic literature study (SLR) on this phenomenon. By collecting the relevant primary studies from Scopus, the results show that the utilisation of blockchain is swiftly maturing with new research and applications published constantly in this domain. However, the ethical discussion related to the use of blockchain technologies is still taking its baby steps in healthcare. Despite a few openings, ethical research is practically non-existing when compared against the full extant literature on the topic. Therefore, remarkable amount of further work is needed to cover the potential ethical questions related to the adoption and use of the technology.

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The Transparency of Big Data, Data Harvesting and Digital Twins

Cited by 9 publications

References 7 publications

ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences

ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

The state of the art of the blockchain ethics in healthcare: A systematic literature review

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