Input and Output Privacy-Preserving Linear Regression

Aono, Yoshinori; Hayashi, Takuya; Phong, Le Trieu; Wang, Lihua

doi:10.1587/transinf.2016inp0019

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…The linear predictor function f (•) can be expressed as In order to alleviate the over-fitting problem of linear regression, its variants (e.g., ridge and lasso regression) are introduced as improved versions of linear regression by adding a regularization term in loss function for more accurate estimates. Using a secure technique combining both HE scheme and differential privacy, Aono et al [188] proposed a privacy-preserving scheme for linear regression. Receiving the encrypted training data from a client (using his public key), the cloud server performs necessary computations on the encrypted data and returns the encrypted model parameters to the client.…”

Section: ) Regression Algorithmsmentioning

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: ) Regression Algorithmsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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“…In section 3.1, the supervised learning module and the feature extraction module of the proposed framework were evaluated using the subspace (ɑ3, ɑ4, ɑ11) of the NSL-KDD data set with binary classes (9,10) only. In a new experiment, the same subspace is again considered; however, the other classes (0,1), (0,5), (1,2), (1,9), (3,5), (3,9), and (6,8) are also studied. In addition, three other subspaces, (ɑ3, ɑ4, ɑ5), (ɑ3, ɑ4, ɑ7), and (ɑ4, ɑ7, ɑ10), are also included in the experiment to study the performance of the proposed analytical framework with DPLR.…”

Section: Multiple Subspace Analysismentioning

confidence: 99%

“…These characteristics of ϵ make the selection of a set of suitable values for ϵ much harder. A significant research has been performed to address this problem in various perspectives [1,2]; however, two main contributions that are closely related to the proposed study are selected and discussed in this section. For example, the authors of [7] studied the contributions of the privacy parameter and proposed a model that provides a balance between the objectives of a data owner and a data user, and studied its effectiveness by selecting several values of privacy parameter.…”

Section: Introductionmentioning

confidence: 99%

Characterization of differentially private logistic regression

Suthaharan

2018

Proceedings of the ACMSE 2018 Conference

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The purpose of this paper is to present an approach that can help data owners select suitable values for the privacy parameter of a differentially private logistic regression (DPLR), whose main intention is to achieve a balance between privacy strength and classification accuracy. The proposed approach implements a supervised learning technique and a feature extraction technique to address this challenging problem and generate solutions. The supervised learning technique selects subspaces from a training data set and generates DPLR classifiers for a range of values of the privacy parameter. The feature extraction technique transforms an original subspace to a differentially private subspace by querying the original subspace multiple times using the DPLR model and the privacy parameter values that were selected by the supervised learning module. The proposed approach then employs a signal processing technique called signal-interference-ratio as a measure to quantify the privacy level of the differentially private subspaces; hence, allows data owner learn the privacy level that the DPLR models can provide for a given subspace and a given classification accuracy.

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“…Aiming to achieve both secrecy and differential privacy, Aono et al [ 39 , 40 ] have designed systems for privacy-preserving linear and logistic regression, in which a semi-honest central server is used to handle homomorphic ciphertexts. Semantic security with homomorphism allows their system to achieve data secrecy (with respect to the central server) and differential privacy (with respect to publishing the final result) simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Secure deep learning for distributed data against maliciouscentral server

Phong

2022

PLoS ONE

Self Cite

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In this paper, we propose a secure system for performing deep learning with distributed trainers connected to a central parameter server. Our system has the following two distinct features: (1) the distributed trainers can detect malicious activities in the server; (2) the distributed trainers can perform both vertical and horizontal neural network training. In the experiments, we apply our system to medical data including magnetic resonance and X-ray images and obtain approximate or even better area-under-the-curve scores when compared to the existing scores.

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Input and Output Privacy-Preserving Linear Regression

Cited by 15 publications

References 19 publications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

Characterization of differentially private logistic regression

Secure deep learning for distributed data against maliciouscentral server

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