Cryptology ePrint Archive: Report 2015/1147

Collusion Resistant Aggregation from Convertible Tags

Iraklis Leontiadis and Ming Li

Abstract: The progress in communication and hardware technology increases the computational capabilities of personal devices. Aggregators, acting as third parties, are interested in learning a statistical function as the sum over a census of data. Users are reluctant to reveal their information in cleartext, since it is treated as personal sensitive information. The paradoxical paradigm of preserving the privacy of individual data while granting an untrusted third party to learn in cleartext a function thereof, is partially addressed by the current privacy preserving aggregation protocols.

Current solutions are either focused on a honest-but-curious Aggregator who is trusted to follow the rules of the protocol or they model a malicious Aggregator with trustworthy users. In this paper we are the first to propose a protocol with fully malicious users who collude with a malicious Aggregator in order to forge a message of a trusted user. We introduce the new cryptographic primitive of \emph{convertible tag}, that consists of a two-layer authentication tag. Users first tag their data with their secret key and then an untrusted \emph{Converter} converts the first layer tags in a second layer. The final tags allow the Aggregator to produce a proof for the correctness of a computation over users' data. Security and privacy of the scheme is preserved against the \emph{Converter} and the Aggregator, under the notions of \emph{Aggregator obliviousness} and \emph{Aggregate unforgeability} security definitions, augmented with malicious users. Our protocol is provably secure and experimental evaluations demonstrate its practicality.

Category / Keywords: data privacy, data security, convertible tags, collusion resistant aggregation

Date: received 26 Nov 2015, last revised 10 Apr 2016, withdrawn 11 Jul 2016

Contact author: leontiad at email arizona edu

Available format(s): (-- withdrawn --)

Version: 20160711:235003 (All versions of this report)

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