SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol

Jiang, Yichen; Hamer, Jenny; Wang, Chenghong; Jiang, Xiaoqian; Kim, Miran; Song, Yongsoo; Xia, Yuhou; Mohammed, Noman; Sadat, Nazmus; Wang, Shuang

doi:10.1109/tcbb.2018.2833463

Cited by 39 publications

(32 citation statements)

References 26 publications

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“…The data is encrypted using Paillier cryptosystem [82] and then put into statistical tests within a secure enclave. Besides these works, more SGX-based schemes are popular in cloud computing and applied in practical fields, like healthcare [83], machine learning [84], [85], data analysis [86], location-based services [87], and many others.…”

Section: Software Guard Extensionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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Section: Software Guard Extensionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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“…In [30] , they propose SecureLR, innovative framework which performs learning and forecasts on biomedical data without damaging the security and data privacy. The model created in [30] depends on homomorphic encryption procedures with hardware security supported through Software Guard Extensions (SGX). Its implementation demonstrates a sensible hybrid cryptographic resolution to deal with vital issues in conducting machine learning with public clouds.…”

Section: After That the Medical Center Uses Aes' Keymentioning

confidence: 99%

“…They also defined logistic regression as a characterization calculation with wide applications in the biomedical informatics, including clinical choice support, hazard evaluation, and sickness categorization. The proposed model in [30] is comprised of four essential elements, with each one performing a task in the SecureLR protocol and performing operations contributing to its functionality and safety. Those elements were Information Proprietors, Cloud Service Providers (CSPs), Authorized Researchers (ARs), and Authentication Service Provider (ASP), which will be presented in more detail below:…”

Section: After That the Medical Center Uses Aes' Keymentioning

confidence: 99%

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A Framework and Cryptography Algorithm for Protecting Sensitive Data on Cloud Service Providers

Alrashidi¹

2019

JKAU Comp IT Sci

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Most companies are sceptical about the security and insurance measures offered by cloud services and are reluctant to store sensitive data, such as employee records, in the cloud. Thus, more effort is needed to support the security of information in cloud computing. This paper proposes a cryptography algorithm called ―random algorithm‖ because it is built on the idea of randomising the encryption of uploaded files among four encryption algorithms. The proposed fragmentation technique helps add security and privacy to cloud storage applications. Based on earlier studies, we have created a file-level fragmentation technique that does not work at the database level, in contrast to commonly employed approaches in the case of fragmentation techniques such as horizontal fragmentation, vertical fragmentation, and hybrid fragmentation, which do work at the database level. The proposed encryption algorithm and fragmentation technique work within an integrated security framework that includes a user authentication gateway that encrypts user registration data through a cryptography algorithm called the RivestShamir-Adleman algorithm (RSA). The results of the proposed security framework were positive, as it contributed to reducing the encryption time and decoding time by approximately 99%, compared to earlier studies

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“…Logistic regression is a well-known binary classification algorithm with categorical dependent features and has been applied to many applications in biomedical informatics, medical decision support, risk estimation, disease categorization, computer vision, marketing, etc. [58]. LR evaluates data statistically and contains one or more independent variables with vital roles to reflect an outcome.…”

Section: Kernel Function Expressionmentioning

confidence: 99%

A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network

2018

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Every day we experience unprecedented data growth from numerous sources, which contribute to big data in terms of volume, velocity, and variability. These datasets again impose great challenges to analytics framework and computational resources, making the overall analysis difficult for extracting meaningful information in a timely manner. Thus, to harness these kinds of challenges, developing an efficient big data analytics framework is an important research topic. Consequently, to address these challenges by exploiting non-linear relationships from very large and high-dimensional datasets, machine learning (ML) and deep learning (DL) algorithms are being used in analytics frameworks. Apache Spark has been in use as the fastest big data processing arsenal, which helps to solve iterative ML tasks, using distributed ML library called Spark MLlib. Considering real-world research problems, DL architectures such as Long Short-Term Memory (LSTM) is an effective approach to overcoming practical issues such as reduced accuracy, long-term sequence dependency, and vanishing and exploding gradient in conventional deep architectures. In this paper, we propose an efficient analytics framework, which is technically a progressive machine learning technique merged with Spark-based linear models, Multilayer Perceptron (MLP) and LSTM, using a two-stage cascade structure in order to enhance the predictive accuracy. Our proposed architecture enables us to organize big data analytics in a scalable and efficient way. To show the effectiveness of our framework, we applied the cascading structure to two different real-life datasets to solve a multiclass and a binary classification problem, respectively. Experimental results show that our analytical framework outperforms state-of-the-art approaches with a high-level of classification accuracy.

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SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol

Cited by 39 publications

References 26 publications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A Framework and Cryptography Algorithm for Protecting Sensitive Data on Cloud Service Providers

A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network

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