Reconstructing curves in three (and higher) dimensional space from noisy data

By Don Coppersmith, Madhu Sudan. 2003

84 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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

1. Yuval Ishai
2. Elette Boyle
3. Amit Sahai
4. Srinath T. V. Setty
5. Rafail M. Ostrovsky
6. Brent Waters
7. Eyal Kushilevitz
8. Dan Boneh

In Computational Theory and Mathematics

1. Yuval Ishai
2. Amit Sahai
3. Srinath T. V. Setty
4. Elette Boyle
5. Rafail M. Ostrovsky
6. Eyal Kushilevitz
7. Laurent Fousse
8. Marc-Olivier Killijian

Citations in the corpus

• XPIR : Private Information Retrieval for Everyone (2016, background): “As number theoretic approaches failed to provide efficient cPIR schemes, some alternatives were explored [6, 7, 8], but all of them were based on non-standard problems and have been broken [9, 10, 11, 12, 13].”
• Cryptography from Anonymity (2006, methodology): “The algorithm from [17] breaks the augmented CR assumption whent > ((n + t)kc)1/(c+1) + k +1.”
• Can We Access a Database Both Locally and Privately? (2017, methodology): “…included standard coding-theoretic noise (that is, if S i were of the form { ( x, p i ( x ) + e i ( x )) : x ∈ F } for a sparse e i ( x )), then standard decoding algorithms (for example Reed-Solomon list-decoding, or the multi-dimensional extension of Coppersmith and Sudan [CS03]) might apply.”