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Helen: Maliciously Secure Coopetitive Learning for Linear Models
[article]
2019
arXiv
pre-print
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In this paper, we design and build Helen, a system that allows multiple parties to train a linear model without revealing their data, a setting we call coopetitive learning. ...
Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m-1 out of m parties. ...
[39] ridge regression no no -Alexandru et al. ...
arXiv:1907.07212v2
fatcat:go2j2nbfyjbx7d62wydrag5hmq
Machine Learning based File Entropy Analysis for Ransomware Detection in Backup Systems
2019
IEEE Access
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With the advent of big data and cloud services, user data has become an important issue. ...
INDEX TERMS Backup system, artificial intelligence, machine learning, malicious code detection, ransomware, entropy, data reliability, data security. ...
Generally, the Ridge regression model is preferred over the Lasso regression model. ...
doi:10.1109/access.2019.2931136
fatcat:3xx53tjaajdavj6uos4dw5j24e
Privacy-preserving Machine Learning as a Service
2018
Proceedings on Privacy Enhancing Technologies
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However, machine learning algorithms require access to the raw data which is often privacy sensitive and can create potential security and privacy risks. ...
We show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. ...
The focus of their solution is on computing linear regression and ridge regression estimates. Their method works for horizontally or vertically distribution of data among parties. ...
doi:10.1515/popets-2018-0024
dblp:journals/popets/HesamifardTGW18
fatcat:s77yrnez7vhzfgao2tweehtery
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
[article]
2022
arXiv
pre-print
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to provide security to the data as well as the model. ...
Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms ...
., [5] proposed a privacypreserving ridge regression by combining Yao garbled circuits with linear homomorphic encryption. ...
arXiv:2208.02587v1
fatcat:v4o4nacmyrbgrekrijfi3lrtj4
Potential Application of Machine Learning in Health Outcomes Research and Some Statistical Cautions
2015
Value in Health
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Researchers using machine learning methods such as lasso or ridge regression should assess these models using conventional specification tests. ...
Traditional analytic methods are often ill-suited to the evolving world of health care big data characterized by massive volume, complexity, and velocity. ...
Machine-learning methods consist of a large number of alternative methods including classification trees, random forests, neural networks, support vector machines, and lasso and ridge regression to name ...
doi:10.1016/j.jval.2014.12.005
pmid:25773546
fatcat:wzkwz7yxuzgc5h3blttxkm4kyq
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
[article]
2017
arXiv
pre-print
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First, we describe a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme ...
---, secure against a honest-but-curious adversary. ...
Scalable and secure logistic regression via
homomorphic encryption. In CODASPY, 2016.
K. Bache and M. Lichman. Uci machine learning repository, 2013. http://archive.ics.
uci.edu/ml.
P.-L. ...
arXiv:1711.10677v1
fatcat:vdx4bzwzgrgnjod6ntsdoifzfa
SecureML: A System for Scalable Privacy-Preserving Machine Learning
2017
2017 IEEE Symposium on Security and Privacy (SP)
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In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent ...
Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation ...
Acknowledgements We thank Jing Huang from Visa Research for helpful discussions on machine learning, and Xiao Wang from University of Maryland for his help on the EMP toolkit. ...
doi:10.1109/sp.2017.12
dblp:conf/sp/MohasselZ17
fatcat:y6lwerwfwnfghjkia76yqenmiy
Fog Computing Based on Machine Learning: A Review
2021
International Journal of Interactive Mobile Technologies
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Sending all this data over the internet will overhead the cloud and consume bandwidth. ...
However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. ...
Finally, with fast deployment, resilient computing capacity, and fast migration, containers are favored over VMs in FC. ...
doi:10.3991/ijim.v15i12.21313
fatcat:ztfuzrshq5eavduujcrhp3unhu
Machine Learning Security: Threats, Countermeasures, and Evaluations
2020
IEEE Access
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training data. ...
In this survey, we systematically analyze the security issues of machine learning, focusing on existing attacks on machine learning systems, corresponding defenses or secure learning techniques, and security ...
., ridge regression, the elastic net, and least absolute shrinkage and selection operator (LASSO). ...
doi:10.1109/access.2020.2987435
fatcat:ksinvcvcdvavxkzyn7fmsa27ji
Improved privacy-preserving training using fixed-Hessian minimisation
[article]
2020
IACR Cryptology ePrint Archive
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To the best of our knowledge, this is the first time homomorphic encryption has been used to implement ridge regression training on encrypted data. ...
training over the BFV homomorphic encryption scheme. ...
Since we are working over encrypted data, we use a linear approximation, T 1 + T 2 d. ...
dblp:journals/iacr/OgilviePR20
fatcat:dl5dm52mpngxzby2pqbcz2jxzm
Matrix Sketching for Secure Collaborative Machine Learning
[article]
2021
arXiv
pre-print
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Collaborative learning allows participants to jointly train a model without data sharing. ...
We propose a practical defense which we call Double-Blind Collaborative Learning (DBCL). ...
Privacy-preserving ridge regression with only linearlyhomomorphic encryption. In International Conference on Applied Cryptography and Network Security, pp. 243-261. Springer, 2018. ...
arXiv:1909.11201v4
fatcat:vojylsyggfhy3amfaqzltv73cu
Input and Output Privacy-Preserving Linear Regression
2017
IEICE transactions on information and systems
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Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). ...
We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. ...
Proof 2: The server receives and computes over encrypted data. ...
doi:10.1587/transinf.2016inp0019
fatcat:s35oz57n7rhczg3oap7g4zy4wm
Privacy-preserving dataset combination and Lasso regression for healthcare predictions
2021
BMC Medical Informatics and Decision Making
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Then, a secure Lasso Regression model is trained on the securely combined data. ...
Conclusions This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. ...
Concerning securely training a linear regression model on distributed data, a lot of work has been done on a variant of linear regression known as Ridge regression. ...
doi:10.1186/s12911-021-01582-y
pmid:34530824
fatcat:r5n7h3hyjzgaxjh55x5ohtc5yq
Deep Learning in Information Security
[article]
2018
arXiv
pre-print
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Machine learning techniques learn models from data representations to solve a task. These data representations are hand-crafted by domain experts. ...
Machine learning has a long tradition of helping to solve complex information security problems that are difficult to solve manually. ...
A fast learning algorithm for deep belief nets. ...
arXiv:1809.04332v1
fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
Extreme Learning Machines [Trends & Controversies]
2013
IEEE Intelligent Systems
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A machine learning algorithm's generalization capability depends on the dataset, which is why engineering a dataset's features to represent the data's salient structure is important. ...
This article proposes a secure and practical outsourcing mechanism called Partitioned ELM to address the challenge of performing the ELM over large-scale data. ...
In "Representational Learning with ELMs for Big Data," the authors propose using the ELM as an auto-encoder for learning feature representations using singular values. ...
doi:10.1109/mis.2013.140
fatcat:xaj2ynxqrnfy5ivzkpkdxxaiia
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