Single Instance Self–masking via Permutations: (Preliminary Version)

Asaf Cohen, Paweł Cyprys, Shlomi Dolev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Self–masking allows the masking of success criteria, part of a problem instance (such as the sum in a subset-sum instance) that restricts the number of solutions. Self–masking is used to prevent the leakage of helpful information to attackers; while keeping the original solution valid and, at the same time, not increasing the number of unplanned solutions. Self–masking can be achieved by xoring the sums of two (or more) independent subset sum instances [4, 5], and by doing so, eliminate all known attacks that use the value of the sum of the subset to find the subset fast, namely, in a polynomial time; much faster than the naive exponential exhaustive search. We demonstrate that the concept of self–masking can be applied to a single instance of the subset sum and a single instance of the permuted secret-sharing polynomials. We further introduce the benefit of permuting the bits of the success criteria, avoiding leakage of information on the value of the i’th bit of the success criteria, in the case of a single instance, or the parity of the i’th bit of the success criteria in the case of several instances. In the case of several instances, we permute the success criteria bits of each instance prior to xoring them with each other. One basic permutation and its nesting versions (e.g., πi ) are used, keeping the solution space small and at the same time, attempting to create an “all or nothing” effect, where the result of a wrong π trials does not imply much.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 7th International Symposium, CSCML 2023, Proceedings
EditorsShlomi Dolev, Ehud Gudes, Pascal Paillier
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031346705
StatePublished - 1 Jan 2023
Event7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023 - Be'er Sheva, Israel
Duration: 29 Jun 202330 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13914 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023
CityBe'er Sheva

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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