Cryptology ePrint Archive: Report 2016/948

Secure Computation in Online Social Networks

Foteini Baldimtsi and Dimitrios Papadopoulos and Stavros Papadopoulos and Alessandra Scafuro and Nikos Triandopoulos

Abstract: Apart from their numerous other benefits, online social networks (OSNs) allow users to jointly compute on each otherís data (e.g., profiles, geo-locations, medical records, etc.). Privacy issues naturally arise in this setting due to the sensitive nature of the exchanged information. Ideally, nothing about a userís data should be revealed to the OSN provider or "non-friend" users, and even her "friends" should only learn the output of a joint computation. In this work we propose the first security framework to capture these strong privacy guarantees for general-purpose computation. We also achieve two additional attractive properties: users do not need to be online while their friends compute on their data, and any user value uploaded at the server can be repeatedly used in multiple computations. We formalize our framework in the setting of secure multi-party computation (MPC) and provide two instantiations: the first is a non-trivial adaptation of garbled circuits that converts inputs under different keys to ones under the same key, and the second is based on two-party mixed protocols and involves a novel two-party re-encryption module. We experimentally validate the efficiency of our instantiations using state-of-the-art tools for two concrete use-cases.

Category / Keywords: cryptographic protocols / Secure multi-party computation, online social network privacy

Date: received 30 Sep 2016

Contact author: dipapado at umd edu

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Version: 20161001:184739 (All versions of this report)

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