Paper published in a journal (Scientific congresses, symposiums and conference proceedings)
Provably Solving the Hidden Subset Sum Problem via Statistical Learning
Coron, Jean-Sébastien; Gini, Agnese
2022In Mathematical Cryptology, 1
Peer reviewed


Full Text
Publisher postprint (417.58 kB)

All documents in ORBilu are protected by a user license.

Send to


Keywords :
Cryptanalysis; lattice reduction; statistical attack; FastICA
Abstract :
[en] At Crypto ’99, Nguyen and Stern described a lattice based algorithm for solving the hidden subset sum problem, a variant of the classical subset sum problem where the n weights are also hidden. As an application, they showed how to break the Boyko et al. fast generator of random pairs (x, g x(mod p)). The Nguyen-Stern algorithm works quite well in practice for moderate values of n, but its complexity is exponential in n. A polynomial-time variant was recently described at Crypto 2020, based on a multivariate technique, but the approach is heuristic only. In this paper, we describe a proven polynomial-time algorithm for solving the hidden subset-sum problem, based on statistical learning. In addition, we show that the statistical approach is also quite efficient in practice: using the FastICA algorithm, we can reach n = 250 in reasonable time.
Disciplines :
Computer science
Author, co-author :
Coron, Jean-Sébastien ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Gini, Agnese  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Coron
External co-authors :
Language :
Title :
Provably Solving the Hidden Subset Sum Problem via Statistical Learning
Publication date :
March 2022
Event name :
MathCrypt 2021
Event date :
Journal title :
Mathematical Cryptology
Special issue title :
Special Issue MathCrypt 2021
Volume :
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 03 August 2021


Number of views
175 (19 by Unilu)
Number of downloads
106 (9 by Unilu)


Similar publications

Contact ORBilu