Secure multiparty computation (SMC) is a promising technology for privacy-preserving collaborative computation. In the last years several feasibility studies have shown its practical applicability in different fields. However, it is recognized that administration, and management overhead of SMC solutions are still a problem [1] [2]. A vital next step is the incorporation of SMC in the emerging fields of the Internet of Things and (smart) dynamic environments. In these settings, the properties of these contexts make utilization of SMC even more challenging since some vital premises for its application regarding environmental stability and preliminary configuration are not initially fulfilled.We bridge this gap by providing FlexSMC, a management and orchestration framework for SMC which supports the discovery of nodes, supports a trust establishment between them and realizes robustness of SMC session by handling nodes failures and communication interruptions. The practical evaluation of FlexSMC shows that it enables the application of SMC in dynamic environments with reasonable performance penalties and computation durations allowing soft real-time and interactive use cases.
In recent years, secure multiparty computation (SMC) advanced from a theoretical technique to a practically applicable technology. Several frameworks were proposed of which some are still actively developed.We perform a first comprehensive study of performance characteristics of SMC protocols using a promising implementation based on secret sharing, a common and state-of-the-art foundation. Therefor, we analyze its scalability with respect to environmental parameters as the number of peers, network properties -namely transmission rate, packet loss, network latency -and parallelization of computations as parameters and execution time, CPU cycles, memory consumption and amount of transmitted data as variables.Our insights on the resource consumption show that such a solution is practically applicable in intranet environments andwith limitations -in Internet settings.
Centralized systems in the Internet of Things-be it local middleware or cloud-based services-fail to fundamentally address privacy of the collected data. We propose an architecture featuring secure multiparty computation at its core in order to realize data processing systems which already incorporate support for privacy protection in the architecture.
Abstract. The improvement of energy efficiency is an important target on all levels of society. It is best achieved on the basis of locally and temporally fine-grained measurement data for identifying unnecessary use of energy. However, at the same time such fine-grained measurements allow deriving information about the persons using the energy. In this paper we describe our work towards a privacy preserving system for energy management. Our solution follows the privacy by design paradigm and uses attribute-based cryptography and virtualization to increase security.
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