(F)unctional Sifting: A Privacy-Preserving Reputation System Through Multi-Input Functional Encryption

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


Functional Encryption (FE) allows users who hold a specific secret key (known as the functional key) to learn a specific function of encrypted data whilst learning nothing about the content of the underlying data. Considering this functionality and the fact that the field of FE is still in its infancy, we sought a route to apply this potent tool to solve the existing problem of designing decentralised additive reputation systems. To this end, we first built a symmetric FE scheme for the ℓ1 norm of a vector space, which allows us to compute the sum of the components of an encrypted vector (i.e. the votes). Then, we utilized our construction, along with functionalities offered by Intel SGX, to design the first FE-based decentralized additive reputation system with Multi-Party Computation. While our reputation system faces certain limitations, this work is amongst the first attempts that seek to utilize FE in the solution of a real-life problem.
Original languageEnglish
Title of host publicationSecure IT Systems
Subtitle of host publication25th Nordic Conference, NordSec 2020, Virtual Event, November 23–24, 2020, Proceedings
EditorsMikael Asplund, Simin Nadjm-Tehrani
Number of pages16
ISBN (Electronic)978-3-030-70852-8
ISBN (Print)978-3-030-70851-1
Publication statusPublished - 2021
Publication typeA4 Article in a conference publication
EventNordic Conference on Secure IT Systems - Virtual, Online
Duration: 23 Nov 202024 Nov 2020

Publication series

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


ConferenceNordic Conference on Secure IT Systems

Publication forum classification

  • Publication forum level 1


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