Secure Linear Regression on Vertically Partitioned Datasets

By Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, David E. Evans. 2016

108 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

SplitNN-driven Vertical Partitioning (2020, background): “Vertically partitioned linear and logistic regression The work in (Gascón et al. , 2016) proposes a multi-party computation (MPC) scheme based on garbled circuits for secure linear regression in the vertically partitioned setting.”
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, methodology): “Among such machine learning models we cite neural networks [31], linear [32, 33] and logistic [34] regression.”
Secure Evaluation of Quantized Neural Networks (2019, methodology): “Arithmetic is handled like in [21] by using a garbled circuit to scale down the data.”
Federated Machine Learning (2019, methodology): “vacy guarantees. Nikolaenko et al.[48] implemented a privacy-preserving protocol for linear regression on horizontally partitioned data using homomorphic encryption and Yao’s garbled circuits and Ref [22, 24] proposed a linear regression approach for vertically partitioned data. These systems solved”
Communication-Efficient Edge AI: Algorithms and Systems (2020, methodology): “ Model partition across edge device and edge server to avoid the exposure of users data [116], [117] Vertical architecture for privacy with vertically partitioned data and model [19], [118], [119], [120], [121], [122], [123] Computation Ofﬂoading Based Edge Inference Systems Server-based edge infere”
Secure and Efficient Federated Transfer Learning (2019, background): “data partitioned in the feature space) has also been studied in depth [5], [6].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Cryptographic methods such as homomorphic encryption [15, 77, 27, 33, 74, 145, 146, 204, 206, 112], and secure multi-party computation (SMC) [157, 36, 23, 51, 24, 101, 17, 62, 91, 181, 39, 65] are widely used in privacy-preserving machine learning algorithms.”
A Secure Federated Transfer Learning Framework (2020, methodology): “Under the assumption that A and B are not allowed to expose their raw data, a privacypreserving approach needs to be developed to compute (3) and (4).”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, methodology): “ty protocol instead. This role is fulfilled by theCSP in Cryptϵ. For this two-server model, we assume semi-honest behaviour and non-collusion. This is a very common assumption in the two-server model [45, 46, 48, 67, 80, 83, 84]. Programming Framework: Conceptually, the aforementioned goal of achiev”
Minimizing latency for secure distributed computing (2017, background): “A large number of users own different amounts of data and a central server aims to train a high-quality model based on all the data with the smallest communication complexity.”
Secure SVM Training Over Vertically-Partitioned Datasets Using Consortium Blockchain for Vehicular Social Networks (2020, background): “Gascon et al. [32] train a linear regression classiﬁer with vertically partitioned datasets by a hybrid protocol.”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, methodology): “The local model G k can take various forms including tree [17], linear and logistic regression [3], [16], [18], [19], [20], [21], support vector machine [22], [23], neural network (NN) [24], [25], [26], K-means [27] and EM algorithm [28].”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, unknown): “[20, 27, 42] are about secure linear regression.”
Data Poisoning Attacks on Federated Machine Learning (2020, methodology): “Several privacy-preserving machine learning methods have been presented for vertically partitioned data, e.g., secure linear regression [8], gradient descent methods [9]; 3) federated transfer learning, i.e., two datasets differ not only in samples but also in feature space.”