Efficient unconditionally secure comparison and privacy preserving machine learning classification protocols

Bernardo David, Rafael Dowsley, Raj Katti, Anderson C.A. Nascimento

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

13 Citations (Scopus)


We propose an efficient unconditionally secure protocol for privacy preserving comparison of ℓ-bit integers when both integers are shared between two semi-honest parties. Using our comparison protocol as a building block, we construct two-party generic private machine learning classifiers. In this scenario, one party holds an input while the other holds a model and they wish to classify the input according to the model without revealing their private information to each other. Our constructions are based on the setup assumption that there exists pre-distributed correlated randomness available to the computing parties, the so-called commodity-based model. The protocols are storage and computationally efficient, consisting only of additions and multiplications of integers.

Original languageEnglish
Title of host publicationProvable Security
Subtitle of host publication9th International Conference, ProvSec 2015 Kanazawa, Japan, November 24–26, 2015 Proceedings
EditorsMan-Ho Au, Atsuko Miyaji
Place of PublicationCham Switzerland
Number of pages14
ISBN (Electronic)9783319260594
ISBN (Print)9783319260587
Publication statusPublished - 2015
Externally publishedYes
EventInternational Conference on Provable Security 2015 - Kanazawa, Japan
Duration: 24 Nov 201526 Nov 2020
Conference number: 9th
https://link.springer.com/book/10.1007/978-3-319-26059-4 (Proceedings)
https://security-lab.jaist.ac.jp/provsec2015/ (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Provable Security 2015
Abbreviated titleProvSec 2015
Internet address


  • Commodity based model
  • Private machine learning
  • Secure comparison
  • Unconditional security

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