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

Alexandros Bakas; Antonis Michalas

Paper 2020/1271

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

Alexandros Bakas and Antonis Michalas

Abstract

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 $ℓ_{1}$ 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.

Metadata

Available format(s): PDF
Category: Secret-key cryptography
Publication info: Published elsewhere. Minor revision. NordSec 2020
Keywords: Functional Encryption Multi-Client Multi-Input Multi-Party Computation Reputation System
Contact author(s): alexandros bakas @ tuni fi
History: 2020-10-14: received
Short URL: https://ia.cr/2020/1271
License: CC BY

BibTeX

@misc{cryptoeprint:2020/1271,
      author = {Alexandros Bakas and Antonis Michalas},
      title = {(F)unctional Sifting: A Privacy-Preserving Reputation System Through Multi-Input Functional Encryption (extended version)},
      howpublished = {Cryptology {ePrint} Archive, Paper 2020/1271},
      year = {2020},
      url = {https://eprint.iacr.org/2020/1271}
}