Allerton 2015 Paper Abstract

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Paper ThA5.4

Mirhoseni, Azalia (Rice University), Koushanfar, Farinaz (Rice University)

Enabling Privacy Preserving Computing at Scale by Modular Signal Processing

Scheduled for presentation during the Invited Session "Security Adversarial Machine Learning" (ThA5), Thursday, October 1, 2015, 09:30−09:50, Lower Level

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on November 19, 2019

Keywords Security and Trust;Statistical Signal Processing

Abstract

Today's data analysis algorithms enjoy the vast amount of available digital content to enhance their performance. Applying analytic on huge data-sets often require offloading parts or all the computation on the cloud. Given that in many scenarios the data to be processed may contain sensitive information, it becomes necessary to create secure and privacy preserving protocols for outsourcing applications. However, the additional overhead of ensuring privacy on large-scale data can be overwhelming. In this work we propose a mechanism that leverages the modular signal property to introduce a light-weight privacy-preserving outsourcing protocol. Our design reduces the costs associated with data encryption/decryption and message passing to and from the server. Our results show that for several types of data we can achieve more than 10 times improvement in memory usage as well as for encryption, decryption, and communication of the data to and from the outsourcing server.

 

 

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