Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products

By Alan F. Karr, Xiaodong Lin, Ashish P. Sanil, Jerome P. Reiter. 2009

133 citations recorded by the source corpus.

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Citations in the corpus

Federated Machine Learning (2019, neutral): “vacy-preserving machine learning algorithms have been proposed for vertically partitioned data, including Cooperative Statistical Analysis [15], association rule mining [65], secure linear regression [22, 32, 55], classification [16] and gradient descent [68]. Recently, Ref [27, 49] proposed a verti”
Secure multiple linear regression based on homomorphic encryption (2011, methodology): “When the decision is to share data covariances matrices, both our approach and the approach of (Karr et al. 2009) provide the same (statistically efficient) answer.”
Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept. (2016, background): “Other algorithms focus on vertically distributed data, meaning that the data centers each hold different parts of the data for the same subject [13,14].”
Secure, Privacy-Preserving Analysis of Distributed Databases (2007, methodology): “By contrast, in the “ofﬁcial statistics” setting of Karr et al. (2004), Karr et al. (2005c), Sanil et al. (2004a) and Sanil et al. (2004b), and the homeland security setting of Karr et al. (2005b), where database owners may be states or multiple federal agencies, conﬁdentiality of data subjects is…”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “Modern MPC protocol implementations are fast enough to securely evaluate complex functions on moderatelylarge data sets, such as the numerous implementations of secure regression analyses with tens to hundreds of thousands of observations, and tens to hundreds of variables [23], [38], [62], [87], [1”
Privacy-preserving matrix factorization (2013, background): “Secret-sharing schemes have been proposed for a variety of linear algebra operations, such as solving a linear system [51], linear regression [29, 30, 21], and auctions [10].”
Fast, Privacy Preserving Linear Regression over Distributed Datasets based on Pre-Distributed Data (2015, background): “Most of them clearly do not even aim at obtaining the level of privacy usually required by modern cryptographic protocols (such as Karr et al. [21] and Du et al.[14], see also [29, 22]).”
Privacy-Preserving Record Linkage (2010, background): “For example, numerous papers already tacitly assume that the files that are input to their procedures are a-priori matched, in the sense that the correspondence between the units is known [24, 23, 22, 12, 13].”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, unknown): “[21, 31, 46] are about secure linear regression.”
Privacy-Preserving Multiple Linear Regression of Vertically Partitioned Real Medical Datasets (2017, methodology): “[9] proposed a secure regression based on the similarity on secure matrix product.”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Applying the above methods, the user privacy of FLSs can usually be well protected [89, 211, 91, 144].”