Secure multiparty computation of approximations

By Joan Feigenbaum, Yuval Ishai, Tal Malkin, Kobbi Nissim, Martin J. Strauss, Rebecca N. Wright. 2001

208 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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In Computational Theory and Mathematics

Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “[27] in an alternative verification phase in which a functionally private approximation of θ is provided to the parties only if the verification test passes.”
Differentially private combinatorial optimization (2009, background): “[14] extend the notion of SFE to NP hard problems for which efficient algorithms must output an approximation to the optimum, unless P=NP.”
Cryptography from Anonymity (2006, background): “6In general, the approximate output together with the randomness used to generate the sketch may reveal a few bits of additional information that do not follow from the exact output (see [19]).”
Fuzzy Keyword Search over Encrypted Data in Cloud Computing (2010, background): “Private matching [14], as another related notion, has been studied mostly in the context of secure multiparty computation to let different parties compute some function of their own data collaboratively without revealing their data to the others.”
Batch codes and their applications (2004, methodology): “In particular, PIR has been used for various special-purpose secure computation tasks such as keyword search [10], distance approximations [13], statistical queries [7], and even for generally compiling a class of communication-efficient protocols into secure ones [23].”
Differentially-Private Multi-Party Sketching for Large-Scale Statistics (2020, background): “An alternative line of work [32, 39] instead studied secure approximation.”
Two Party Distribution Testing: Communication and Security (2018, methodology): “This is a framework introduced by [FIM + 06], allowing to combine the beneﬁts of approximation algorithms and secure computation.”
A Random Rotation Perturbation Approach to Privacy Preserving Data Classification (2005, methodology): “We prove that the three types of classifiers will deliver the same performance over the rotation perturbed dataset as over the original dataset.”
A Constraint Satisfaction Cryptanalysis of Bloom Filters in Private Record Linkage (2011, background): “Unlike SMC, transformation can leave data vulnerable to compromise due to the presence of partial information.”
Compressed Regression (2007, background): “Finall y, it is important to mention the active area of cryptographic approaches to privacy from the theore tical computer science community, for instance [9, 7]; however, this line of work is quite differen t from our approach.”
Efficient Private Matching and Set Intersection (2004, methodology): “We refer the reader to [10] for the formal definition.”
Efficient Similarity Search over Encrypted Data (2012, methodology): “We present an overview of LSH in Part III-A and we continue with the security deﬁnition in Part III-B.”
Robust Information-Theoretic Private Information Retrieval (2002, background): “Other works which use PIR protocols are [53], [20], and [28].”
Computational Differential Privacy (2009, unknown): “[35]).”
Practical privacy: the SuLQ framework (2005, background): “To our best knowledge, the only attempts to de.ne such functional privacy in the context of secure function evaluation was by Feigenbaum, Ishai, Malkin, Nissim, Strauss, and Wright [9] in de.ning private approximations, and similarly Halevi, Kushilevitz, Krauthgamer, and Nissim [10] in de.ning alm”