Secure multi-party computation problems and their applications: a review and open problems

By Wenliang Du, Mikhail J. Atallah. 2001

538 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Crypto-Nets: Neural Networks over Encrypted Data (2014, background): “However, this could be addressed by secure multi-party computation (Du & Atallah, 2001).”
Performance Comparison of Secure Comparison Protocols (2009, background): “SMC protocols have widespread applications [4], [5] in various real-life problems, such as matchmaking, electronic voting, private bidding and auctions, etc.”
PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks (2007, unknown): “Another class of privacy-preserving data mining schemes [22], [23], [24] is based on Secure Multi-party Computation (SMC) techniques [25], [26], [27].”
The new Casper: query processing for location services without compromising privacy (2006, background): “Several architectures have been explored to provide secure data transformation from the client to the server machines (e.g., secure-multi-party communication [12], minimal information sharing [3], untrusted third party [14], and trusted third party [2, 23]).”
Privacy-Preserving Inter-database Operations (2004, background): “Du and Atallah’s paper [11] provides a good review of secure multi-party computation (SMC) problems and develops a framework to identify new SMC problems.”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, background): “The work presented in [ 25 ] introduced a variety of new SMC applications and open problems for a spectrum of cooperative computation domains.”
Privacy preserving frequent itemset mining (2002, methodology): “This method is based on secure multi-party computation (Du & Atallah 2001) and incorporates cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.”
Privacy preserving regression modelling via distributed computation (2004, background): “This need to balance the utility of better combined analyses with the risk of privacy violation has received considerable interest lately [1, 5].”
State-of-the-art in privacy preserving data mining (2004, background): “The first one [ 11 ] proposes a transformation framework that allows to systematically transform normal computations to secure multiparty computations.”
Privacy-preserving matrix factorization (2013, methodology): “A survey of algorithms for which efficient data-oblivious implementations are unknown can be found in [11]: matrix factorization broadly falls into the category of Data Mining summarization problems.”
Secure and practical outsourcing of linear programming in cloud computing (2011, background): “…be very inefﬁcient, Du and Atallah et. al. have proposed a series of customized solutions under the SMC context to a spectrum of special computation problems, such as privacy-preserving cooperative statistical analysis, scientiﬁc computation, geometric computations, sequence comparisons, etc. [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.”
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”
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, methodology): “Another approach would be to use Multi-Party Computation (MPC) known from cryptography [141].”