20 20

Transactions on
Data Privacy
Foundations and Technologies

http://www.tdp.cat


Articles in Press

Accepted articles here

Latest Issues

Year 2021

Volume 14 Issue 1

Year 2020

Volume 13 Issue 3
Volume 13 Issue 2
Volume 13 Issue 1

Year 2019

Volume 12 Issue 3
Volume 12 Issue 2
Volume 12 Issue 1

Year 2018

Volume 11 Issue 3
Volume 11 Issue 2
Volume 11 Issue 1

Year 2017

Volume 10 Issue 3
Volume 10 Issue 2
Volume 10 Issue 1

Year 2016

Volume 9 Issue 3
Volume 9 Issue 2
Volume 9 Issue 1

Year 2015

Volume 8 Issue 3
Volume 8 Issue 2
Volume 8 Issue 1

Year 2014

Volume 7 Issue 3
Volume 7 Issue 2
Volume 7 Issue 1

Year 2013

Volume 6 Issue 3
Volume 6 Issue 2
Volume 6 Issue 1

Year 2012

Volume 5 Issue 3
Volume 5 Issue 2
Volume 5 Issue 1

Year 2011

Volume 4 Issue 3
Volume 4 Issue 2
Volume 4 Issue 1

Year 2010

Volume 3 Issue 3
Volume 3 Issue 2
Volume 3 Issue 1

Year 2009

Volume 2 Issue 3
Volume 2 Issue 2
Volume 2 Issue 1

Year 2008

Volume 1 Issue 3
Volume 1 Issue 2
Volume 1 Issue 1


Volume 13 Issue 1


Secure Multi-party Summation Protocols: Are They Secure Enough Under Collusion?

Thilina Ranbaduge(a),(*), Dinusha Vatsalan(b), Peter Christen(a)

Transactions on Data Privacy 13:1 (2020) 25 - 60

Abstract, PDF

(a) Research School of Computer Science, The Australian National University, Canberra ACT 2601, Australia.

(b) Information Security and Privacy Group, Data61-CSIRO, Sydney NSW 2015, Australia.

e-mail:thilina.ranbaduge @anu.edu.au; ;


Abstract

To enable data analytics that provides valuable insights, data that are distributed across several organisations increasingly need to be shared before they can be analysed. However, sharing data from different sources can raise privacy and confidentiality concerns. Organisations are often unwilling or not allowed to share their sensitive data, such as personal details or health or financial data, with other parties because this potentially violates the privacy of individuals. Secure multi-party computation (SMC) has been introduced as a solution to overcome the problem of performing computations on sensitive data across organisations. SMC allows parties to jointly compute a function over their inputs while preserving the privacy of these inputs. Secure summation protocols are an important building block in many SMC applications that can be used under two different SMC models (i.e. with and without the involvement of a third party to conduct the computations). A secure summation protocol is used to compute the summation of private inputs held by different parties. In this paper we study existing secure summation protocols that can be used under different SMC models and then propose three advanced secure summation protocols that use homomorphic encryption. We then consider different scenarios of how parties might collude with each other in secure summation protocols, and the potential collusion risks that occur with these protocols. No such investigation of possible collusion scenarios for secure summation protocols has so far been presented. We analyse each secure summation protocol under different collusion scenarios and evaluate the efficiency of each protocol with different numbers of parties and different input data sizes. Our evaluation shows that our proposed protocols provide improved privacy against collusion risks and they can calculate a sum more efficiently compared to existing secure summation protocols.

* Corresponding author.

Follow us




Supports




ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; Vicenç Torra; U. of Skövde; PO Box 408; 54128 Skövde; (Sweden); e-mail:tdp@tdp.cat
Note: TDP's web site does not use cookies. TDP does not keep information neither on IP addresses nor browsers. For the privacy policy access here.

 


Vicenç Torra, Last modified: 00 : 08 May 19 2020.