Cryptology ePrint Archive: Report 2014/256

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Iraklis Leontiadis, Kaoutar Elkhiyaoui, Refik Molva

Abstract: Abstract. With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [7] recently suggested a solution that allows a centralized party to compute the sum of encrypted inputs collected through a smart metering network. The main shortcomings of this solution are its reliance on a trusted dealer for key distribution and the need for frequent key updates. In this paper we introduce a secure protocol for aggregation of time series data that is based on the Joye et al. [7] scheme and in which the main shortcomings of the latter, namely, the requirement for key updates and for the trusted dealer are eliminated. Moreover our scheme supports a dynamic group management, whereby as opposed to Joye et al. [7] leave and join operations do not trigger a key update at the users.

Category / Keywords: data aggregation, privacy, time-series data

Original Publication (with minor differences): CANS 2014

Date: received 10 Apr 2014, last revised 20 Feb 2015

Contact author: leontiad at eurecom fr

Available format(s): PDF | BibTeX Citation

Version: 20150220:172259 (All versions of this report)

Short URL:

[ Cryptology ePrint archive ]