Together or Alone: The Price of Privacy in Collaborative Learning

Abstract Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium.

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The impact of Negative to Positive Training Dataset Ratio on Atrial Fibrillation Classification Machine Learning Algorithms Performance

Journal of Physics Conference Series ◽

10.1088/1742-6596/1500/1/012131 ◽

2020 ◽

Vol 1500 ◽

pp. 012131

Author(s):

Firdaus ◽

Andre Herviant Juliano ◽

Naufal Rachmatullah ◽

Sarifah Putri Rafflesia ◽

Dinna Yunika Hardiyanti ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Dataset ◽

The Impact

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A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

Energies ◽

10.3390/en14185718 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5718

Author(s):

Regelii Suassuna de Andrade Ferreira ◽

Patrick Picher ◽

Hassan Ezzaidi ◽

Issouf Fofana

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Training Dataset ◽

Response Analysis ◽

Learning Approach ◽

Influence Of Temperature ◽

Machine Learning Approach ◽

Electrical Faults ◽

Transformer Model ◽

The Impact

Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

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Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms

PeerJ Computer Science ◽

10.7717/peerj-cs.547 ◽

2021 ◽

Vol 7 ◽

pp. e547

Author(s):

Aijaz Ahmad Reshi ◽

Imran Ashraf ◽

Furqan Rustam ◽

Hina Fatima Shahzad ◽

Arif Mehmood ◽

...

Keyword(s):

Machine Learning ◽

Medical Diagnosis ◽

Sampling Technique ◽

Machine Learning Algorithms ◽

Diagnostic Methods ◽

Classification Model ◽

Superior Performance ◽

Training Dataset ◽

Tree Classifier ◽

The Impact

Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F1 score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique.

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Homology-based prediction of resistance to antituberculous medications using machine learning algorithms

10.21203/rs.2.18791/v1 ◽

2019 ◽

Author(s):

Mohamed Dafaalla ◽

Mohamed Omer Elsadiq Abdullah ◽

Sahar Bakhiet ◽

Muntasir Ibrahim

Keyword(s):

Machine Learning ◽

Drug Resistance ◽

Predictive Value ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Dataset ◽

Solid Base ◽

K Nearest Neighbors ◽

Linear Discriminant ◽

The Impact

Abstract Objectives: We aimed to develop a prediction model based on machine learning algortihms to predict the impact of variants on resistance of Mycobacterium. Data was collected from TB Drug Resistance Database (TBDReaMDB), and the drug sensitive variants from GMTV database. We also collected a List of 1115 NsSNPS reported in proteins related to drug resistance to Rifampicin, Isoniazid, Pyrazinamide and Ethambutol. PMUT online tool was used to generate the features included in the algorithm training. We trained different classifiers using R software on the features generated by PMUT. The classifiers trained are Random Forrest, Boosting prediction, Naive Bayes, Neural networks, k-Nearest Neighbors, Logistic regression, and Linear Discriminant analysis.Results: The 445 variants valid for comparison were divided into training dataset (75%) and testing dataset (25%). We compared the classifiers according to the AUC, accuracy, kappa, sensitivity, specificity, positive predictive value, and negative predictive value. Results show that random forrest is the best classifier (accuracy: 0.9072. Kappa: 0.690, Sensitivity: 1.00, Specificity : 0.5909, Pos-Pred Value : 0.8929, Neg-Pred Value : 1.00, Detection Rate : 0.773). This indicates that Homology-based machine learning algorithms could be a solid base for development of rapid tools for screening of M.TB resistance to medications.

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Derivation of Respiratory Metrics in Health and Asthma; Machine Learning Methodology (Preprint)

10.2196/preprints.25178 ◽

2020 ◽

Author(s):

Joseph Prinable ◽

Peter Jones ◽

David Boland ◽

Alistair McEwan ◽

Cindy Thamrin

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Reference Signal ◽

Machine Learning Algorithms ◽

Training Dataset ◽

Local Health ◽

Strongly Correlated ◽

Human Research Ethics ◽

Expiration Time ◽

Wide Variability

BACKGROUND The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. OBJECTIVE Examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. METHODS Pulse oximetry data was collected from 11 healthy and 11 asthma subjects who breathed at a range of controlled respiratory rates. UNET and Long Short-Term memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. RESULTS The UNET vs LSTM model provided breathing metrics which were strongly correlated with those from the reference signal (all p<0.001, except for inspiratory:expiratory ratio). The following relative mean bias(95% confidence interval) were observed: inspiration time 1.89(-52.95, 56.74)% vs 1.30(-52.15, 54.74)%, expiration time -3.70(-55.21, 47.80)% vs -4.97(-56.84, 46.89)%, inspiratory:expiratory ratio -4.65(-87.18, 77.88)% vs -5.30(-87.07, 76.47)%, inter-breath intervals -2.39(-32.76, 27.97)% vs -3.16(-33.69, 27.36)%, and respiratory rate 2.99(-27.04 to 33.02)% vs 3.69(-27.17 to 34.56)%. CONCLUSIONS Both machine learning models show strongly correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g. by increasing the size of the training dataset at the lower breathing rates. CLINICALTRIAL Sydney Local Health District Human Research Ethics Committee (#LNR\16\HAWKE99 ethics approval).

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Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Remote Sensing ◽

10.3390/rs13010010 ◽

2020 ◽

Vol 13 (1) ◽

pp. 10

Author(s):

Andrea Sulova ◽

Jamal Jokar Arsanjani

Keyword(s):

Climate Change ◽

Machine Learning ◽

Random Forest ◽

Google Earth ◽

Summer Season ◽

Driving Factors ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Training Dataset ◽

Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

Future Internet ◽

10.3390/fi13030063 ◽

2021 ◽

Vol 13 (3) ◽

pp. 63

Author(s):

Maghsoud Morshedi ◽

Josef Noll

Keyword(s):

Machine Learning ◽

Network Performance ◽

Service Providers ◽

Video Conferencing ◽

Machine Learning Algorithms ◽

Performance Parameters ◽

High Definition ◽

Internet Service ◽

Novel Approach ◽

5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

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Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Bioinformatics ◽

10.1093/bioinformatics/btv674 ◽

2015 ◽

Vol 32 (6) ◽

pp. 821-827 ◽

Cited By ~ 19

Author(s):

Enrique Audain ◽

Yassel Ramos ◽

Henning Hermjakob ◽

Darren R. Flower ◽

Yasset Perez-Riverol

Keyword(s):

Machine Learning ◽

Isoelectric Point ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Basis Set ◽

Superior Performance ◽

Supplementary Information ◽

Training Dataset ◽

Accurate Estimation ◽

Prediction Methods

Abstract Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: [email protected] Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

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