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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Leontiadis, I., Elkhiyaoui, K., Molva, R. (2014). Private and Dynamic Time-Series Data Aggregation with Trust Relaxation. In: Gritzalis, D., Kiayias, A., Askoxylakis, I. (eds) Cryptology and Network Security. CANS 2014. Lecture Notes in Computer Science, vol 8813. Springer, Cham. https://doi.org/10.1007/978-3-319-12280-9_20
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DOI: https://doi.org/10.1007/978-3-319-12280-9_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12279-3
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