Paper 2014/256

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Iraklis Leontiadis, Kaoutar Elkhiyaoui, and Refik Molva


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.

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Published elsewhere. MINOR revision.CANS 2014
data aggregationprivacytime-series data
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leontiad @ eurecom fr
2015-02-20: last of 5 revisions
2014-04-20: received
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      author = {Iraklis Leontiadis and Kaoutar Elkhiyaoui and Refik Molva},
      title = {Private and Dynamic Time-Series Data Aggregation with Trust Relaxation},
      howpublished = {Cryptology ePrint Archive, Paper 2014/256},
      year = {2014},
      doi = {10.1007/978-3-319-12280-9_20},
      note = {\url{}},
      url = {}
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