Privacy-preserving cooperative scientific computations

By Wenliang Du, Mikhail J. Atallah. 2001

204 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “Questions of privacy-preserving data mining and private computation of machine learning algorithms have been considered in several works [19, 40, 48, 71], providing theoretical protocols without implementations or practical evaluations of their efficiency.”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, methodology): “Secure 2-party computation on dot product of two vectors was originally studied by Du and Atallah [3, 10], and further studied by Vaidya and Clifton [25].”
Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products (2009, methodology): “The technique introduced in Du and Atallah (2001) utilizes the “1 of out N” oblivious transfer protocol.”
SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017, unknown): “Earlier work on privacy preserving machine learning has focused on decision trees [31], k-means clustering [28], [14], SVM classification [48], [44], linear regression [19], [20], [40] and logistic regression [42].”
A survey on trust management for Internet of Things (2014, background): “It is widely used in privacy preserving collaborative computation: privacy preserving computational geometry [44], privacy preserving statistic analysis [45], privacy preserving matrix computation [46], privacy preserving scientific computation [47], especially privacy preserving data mining [48-52]”
Tools for privacy preserving distributed data mining (2002, background): “Some other examples can be found in [3; 6].”
Secure Linear Regression on Vertically Partitioned Datasets (2016, background): “Questions of privacy preserving data mining and private computation of machine learning algorithms including linear regression have been considered in several works [12, 14, 23, 27, 38, 41] which offer theoretical protocols without implementation and practical evaluations of their efficiency.”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “broad potential for practical applications, ranging from general computations of secure statistical analysis [22], [23], [25], [52], [53], [54], [55], [96], to more domainspecific uses like financial oversight [2], [21], [26], [58], biomedical computations [35], [32], [75], [84], [82], [85], [132] a”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, unknown): “These problems include privacy preserving information retrieval [26], privacy preserving geometric computation [27], privacy preserving statistical analysis [28], [29], and privacy preserving scientific computations [ 30 ], etc.”
ABY3: A Mixed Protocol Framework for Machine Learning (2018, methodology): “Earlier work on privacy preserving machine learning considered decision trees [39], k-means clustering [15, 35], SVM classification [56, 58], linear regression [25, 26, 49] and logistic regression [52].”
Secure multi-party computation problems and their applications: a review and open problems (2001, methodology): “The authors of this paper have already studied some of these problems [12, 13, 14, 4].”
A privacy-preserving protocol for neural-network-based computation (2006, methodology): “For the time being, we are not interested in the particular implementation of the PPSP, we only need to know that several solutions have been proposed in the relevant literature including those described in [1, 13].”
Reversible Image Watermarking Using Interpolation Technique (2010, unknown): “(cid:5)(cid:4) (cid:16) (cid:9) (cid:0) (cid:0) (cid:0) (cid:3)(cid:4) (cid:0) (cid:16) (cid:15)(cid”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “Protocols for matrix operations including solving linear equations can be traced back to early works of Du and Atallah [57] where the authors use various algebraic masking techniques to reformulate the problem so that the main bulk”