Securing Fast Learning! Ridge Regression over Encrypted Big Data

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Chow, Sherman S. M.; Zou, Qin

doi:10.1109/trustcom.2016.0041

Cited by 17 publications

(13 citation statements)

References 7 publications

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“…Moreover, Hu et. al., [29] gives a solution on the privacy-preserving linear regression problem via Gaussian elimination and Jacobi iteration and then transform the linear regression problem into solving linear equations.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Locally Weighted Linear Regression Over Encrypted Millions of Data

et al. 2020

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The emerging development of cloud computing makes a trend that the cloud becomes a outsourced agglomeration for storing big data that generally contains numerous information. To mine the rich value involved in big data, the machine learning methodology is widespread employed due to its ability to adapt to data changes. However, the data mining process may involve the privacy issues of the users, hence they are reluctant to share their information. This is the reason why the outsourced data need to be dealt with securely, where data encryption is considered to be the most straightforward method to keep the privacy of data, but machine learning on the data in ciphertext domain is more complicated than the plaintext, since the relationship structure between data is no longer maintained, in such a way that we focus on the machine learning over encrypted big data. In this work, we study locally weighted linear regression (LWLR), a widely used classic machine learning algorithm in real-world, such as predict and find the best-fit curve through numerous data points. To tackle the privacy concerns in utilizing the LWLR algorithm, we present a system for privacy-preserving locally weighted linear regression, where the system not only protects the privacy of users but also encrypts the best-fit curve. Therefore, we use Paillier homomorphic encryption as the building modular to encrypt data and then apply the stochastic gradient descent in encrypted domain. After given a security analysis, we study how to let Paillier encryption deal with real numbers and implement the system in Python language with a couple of experiments on real-world data sets to evaluate the effectiveness, and show that it outperforms the state-of-the-art and occurs negligible errors compared with performing locally weighted linear regression in the clear. INDEX TERMS Locally weighted linear regression, privacy-preserving, paillier homomorphic encryption, stochastic gradient descent. HAIFENG QIAN received the B.S. and master's degrees in algebraic geometry from the

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Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Locally Weighted Linear Regression Over Encrypted Millions of Data

et al. 2020

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“…Using SWHE, Bos et al [32] aim to evaluate known predictive models, such as logistic regression and proportional hazards models, on encrypted medical data. Hu et al [33] aim to support ridge regression.…”

Section: Other Related Approachesmentioning

confidence: 99%

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Aloufi

Wong

et al. 2019

IEEE Trans. Dependable and Secure Comput.

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Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate a decision tree in a two-party setting where the feature vector of the client or the decision tree model (such as the threshold values of its nodes) is kept secret from another party. However, these works cannot be extended trivially to support the outsourcing setting where a third-party who should not have access to the model or the query. Furthermore, their use of an interactive comparison protocol does not support branching program, hence requires interactions with the client to determine the comparison result before resuming the evaluation task. In this paper, we propose the first secure protocol for collaborative evaluation of random forests contributed by multiple owners. They outsource evaluation tasks to a third-party evaluator. Upon receiving the client's encrypted inputs, the cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result. The system is based on our new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption. This allows us to reduce communication overheads while achieving round complexity lower than existing work.

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“…Compared with previous work [191], the work abandons the use of Yao's protocol, and only adopts LHE properties to compute the system of linear equations Aw = b, where matrix A and vector b are the encrypted known parameters, and the vector w is the model parameter which can be securely calculated. Hu et al [193] designed an efficient multiplication protocol over encrypted real numbers utilizing Paillier encryption. Based on the multiplication protocol, the authors further presented a lightweight and privacy-preserving ridge regression scheme.…”

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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Securing Fast Learning! Ridge Regression over Encrypted Big Data

Cited by 17 publications

References 7 publications

Privacy-Preserving Locally Weighted Linear Regression Over Encrypted Millions of Data

Privacy-Preserving Locally Weighted Linear Regression Over Encrypted Millions of Data

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

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