Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

  • Iraklis Leontiadis
  • Kaoutar Elkhiyaoui
  • Refik Molva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8813)


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.


data aggregation privacy time-series data 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Iraklis Leontiadis
    • 1
  • Kaoutar Elkhiyaoui
    • 1
  • Refik Molva
    • 1
  1. 1.EURECOMSophia AntipolisFrance

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