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Open AccessArticle

Supporting Privacy of Computations in Mobile Big Data Systems

Qualcomm Research, Santa Clara, CA 95051, USA
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
Author to whom correspondence should be addressed.
This paper is an extended version of our work published in the 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC’14), Niagara Falls, ON, Canada, 17–20 August 2014.
Academic Editors: Eduardo Fernández-Medina Patón and David G. Rosado
Future Internet 2016, 8(2), 17;
Received: 16 February 2016 / Revised: 26 April 2016 / Accepted: 28 April 2016 / Published: 10 May 2016
(This article belongs to the Special Issue Security in Cloud Computing and Big Data)
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. View Full-Text
Keywords: secure cloud computing; privacy preserving search; garbled circuits; secure multiparty computation secure cloud computing; privacy preserving search; garbled circuits; secure multiparty computation
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MDPI and ACS Style

Nandha Premnath, S.; Haas, Z.J. Supporting Privacy of Computations in Mobile Big Data Systems. Future Internet 2016, 8, 17.

AMA Style

Nandha Premnath S, Haas ZJ. Supporting Privacy of Computations in Mobile Big Data Systems. Future Internet. 2016; 8(2):17.

Chicago/Turabian Style

Nandha Premnath, Sriram; Haas, Zygmunt J. 2016. "Supporting Privacy of Computations in Mobile Big Data Systems" Future Internet 8, no. 2: 17.

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