文章基本信息

标题：Supporting Privacy of Computations in Mobile Big Data Systems
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作者：Sriram Nandha Premnath ; Zygmunt J. Haas
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2016
卷号：8
期号：2
页码：17-41
DOI：10.3390/fi8020017
出版社：MDPI Publishing
摘要：Cloud computing systems enable clients to rent and share computing resources of third party platforms, and have gained widespread use in recent years. Numerous varieties of mobile, small-scale devices such as smartphones, red e-health devices, etc., across users, are connected to one another through the massive internetwork of vastly powerful servers on the cloud. While mobile devices store “private information” of users such as location, payment, health data, etc., they may also contribute “semi-public information” (which may include crowdsourced data such as transit, traffic, nearby points of interests, etc.) for data analytics. In such a scenario, a mobile device may seek to obtain the result of a computation, which may depend on its private inputs, crowdsourced data from other mobile devices, and/or any “public inputs” from other servers on the Internet. We demonstrate a new method of delegating real-world computations of resource-constrained mobile clients using an encrypted program known as the garbled circuit. Using the garbled version of a mobile client’s inputs, a server in the cloud executes the garbled circuit and returns the resulting garbled outputs. Our system assures privacy of the mobile client’s input data and output of the computation, and also enables the client to verify that the evaluator actually performed the computation. We analyze the complexity of our system. We measure the time taken to construct the garbled circuit as well as evaluate it for varying number of servers. Using real-world data, we evaluate our system for a practical, privacy preserving search application that locates the nearest point of interest for the mobile client to demonstrate feasibility.
关键词：secure cloud computing; privacy preserving search; garbled circuits; secure multiparty computation secure cloud computing ; privacy preserving search ; garbled circuits ; secure multiparty computation