Privacy-preserving cooperative statistical analysis

By Wenliang Du, Mikhail J. Atallah. 2001

330 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Federated Machine Learning (2019, background): “d to all participants. 2.3.2 Vertical Federated Learning. Privacy-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 g”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “analysis and regression [5], [6], [7], [8], [9], filtered sums,”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “ecurity model [81], which can make the system more practical. Related to privacy level, the threat models also vary in FLSs. A common assumption is that all parties are honest-but-curious [11], [16], [82], meaning that they follow the protocol but try to ﬁnd out as much as possible about the data of”
Securely outsourcing linear algebra computations (2010, background): “All these metrics considered in [12] were between strings that have the same length as each other – it is indeed a limitation of the techniques in [12] that they do not extend to the present situation where the strings are of different length and insertions and deletions are part of the definition.”
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]”
Secure Matrix Multiplication with MapReduce (2017, neutral): “Distributed matrix multiplication has been thoroughly investigated in the secure multi-party computation model (MPC) [9, 1, 20, 10], whose goal is to allow diﬀerent nodes to jointly compute a function over their private inputs without revealing them.”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, background): “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.”
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”