Data Anonymisation, Outlier Detection and Fighting Overfitting with Restricted Boltzmann Machines

Kondratyev, Alexei; Schwarz, Christian; Horvath, Blanka

doi:10.2139/ssrn.3526436

Cited by 8 publications

(11 citation statements)

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“…Furthermore, there are no links between the units of each layer [15]. The training dataset is presented to the network's visible layer, while the network's hidden layer learns how to represent these data in a low-dimensional probabilistic way [23]. For every pairing of visible and hidden units, the network assigns the following Boltzmann probability distribution 𝑃(𝑣, ℎ) [19]:…”

Section: Restricted Boltzmann Machines (Rbms)mentioning

confidence: 99%

Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

Alshammari,

El Hindi

2024

Applied Sciences

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The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.

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Section: Restricted Boltzmann Machines (Rbms)mentioning

confidence: 99%

Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

Alshammari,

El Hindi

2024

Applied Sciences

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“…This section outlines a very brief summary of some potential applications of generative modelling based market simulation (market generators) that can go beyond the currently widespread standard applications of numerical simulations of market paths. For a more detailed study see [45] and [33] for an application to anomaly detection.…”

Section: Possible Applications That Call For Generative Simulation Of...mentioning

confidence: 99%

“…Also the level of anonymity achieved by this procedure is a highly interesting question on its own. The study presented in [45] is devoted to understanding these questions in more detail.An even more challenging situation arises if the size available data sample to train the generative model is very small to begin with:…”

Section: Possible Applications That Call For Generative Simulation Of...mentioning

confidence: 99%

“…These applications incldude data anonymisation techniques, or the identification of relevant properties (anomalies and outliers) of available data, as well as sophisticated backtesting and risk management strategies. Section 1 contains a brief summary of such applications, for a more detailled study see [45]. In Section 2 we contrast the main differences of traditional path generation of stochastic processes with the generative approach and discuss new challenges that arise if numerical time series are simulated by generative models rather than traditional means.…”

Section: Introductionmentioning

confidence: 99%

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A Data-driven Market Simulator for Small Data Environments

Bühler¹,

Horvath²,

Lyons³

et al. 2020

Preprint

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Neural network based data-driven market simulation unveils a new and flexible way of modelling financial time series, without imposing assumptions on the underlying stochastic dynamics. Though in this sense generative market simulation is model-free, the concrete modelling choices are nevertheless decisive for the features of the simulated paths. We give a brief overview of currently used generative modelling approaches and performance evaluation metrics for financial time series, and address some of the challenges to achieve good results in the latter. We also contrast some classical approaches of market simulation with simulation based on generative modelling and highlight some advantages and pitfalls of the new approach. While most generative models tend to rely on large amounts of training data, we present here a generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we show how a rough paths perspective combined with a parsimonious Variational Autoencoder framework provides a powerful way for encoding and evaluating financial time series in such environments where available training data is scarce. Finally, we also propose a suitable performance evaluation metric for financial time series and discuss some connections of our Market Generator to deep hedging.

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“…Further variations may come according to the modeller's choice of framing the problem; for example, one may wish to segment regimes via change points detection methods, which are place (as the name sugests) emphasis on change-points rather than on the regimes themselves (see [NHZ16] for an overview). Another example is the more general outlier detection problem [CFLA20,KSH20], which is a special one-class case of the MRCP. Those studying such a problem are often more interested in identifying anomalous datum, as opposed to characterising the distribution µ ∈ P(R) such datum are generated from.…”

Section: Introductionmentioning

confidence: 99%

Clustering Market Regimes using the Wasserstein Distance

Horvath¹,

Issa²,

Muguruza³

2021

Preprint

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The problem of rapid and automated detection of distinct market regimes is a topic of great interest to financial mathematicians and practitioners alike. In this paper, we outline an unsupervised learning algorithm for clustering financial time-series into a suitable number of temporal segments (market regimes). As a special case of the above, we develop a robust algorithm that automates the process of classifying market regimes. The method is robust in the sense that it does not depend on modelling assumptions of the underlying time series as our experiments with real datasets show. This method -dubbed the Wasserstein k-means algorithm -frames such a problem as one on the space of probability measures with finite p th moment, in terms of the p-Wasserstein distance between (empirical) distributions. We compare our WK-means approach with a more traditional clustering algorithms by studying the so-called maximum mean discrepancy scores between, and within clusters. In both cases it is shown that the WK-means algorithm vastly outperforms all considered competitor approaches. We demonstrate the performance of all approaches both in a controlled environment on synthetic data, and on real data.

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Data Anonymisation, Outlier Detection and Fighting Overfitting with Restricted Boltzmann Machines

Cited by 8 publications

References 0 publications

Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

A Data-driven Market Simulator for Small Data Environments

Clustering Market Regimes using the Wasserstein Distance

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