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Authors: Roger A. Hallman ; Mamadou H. Diallo ; Michael A. August and Christopher T. Graves

Affiliation: SPAWAR Systems Center Pacific, United States

Keyword(s): Big Data, Homomorphic Encryption, Data Privacy, Secure Computation.

Abstract: With the ubiquity of mobile devices and the emergence of Internet of Things (IoT) technologies, most of our activities contribute to ever-growing data sets which are used for big data analytics for a variety of uses, from targeted advertising to making medical and financial judgments and beyond. Many individuals and organizations adopt this new big data paradigm without giving any consideration to privacy and security when they create this data and voluntarily give it up for aggregation. Data breaches have become such a common occurrence that it is easy to despair that concepts like privacy and security are antiquated and we should simply accept data leakage as a new normal. Homomorphic Encryption (HE) is a method of secure computation which allows for calculations to be made on encrypted data without decrypting it and without giving away information about the operations being done. While HE has historically been plagued by computational inefficiencies, the field is rapidly advancing to a point where it is efficient enough for practical use in limited settings. In this paper, we argue that, with sufficient investment, HE will become a practical tool for secure processing of big data sets. (More)

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Paper citation in several formats:
Hallman, R.; Diallo, M.; August, M. and Graves, C. (2018). Homomorphic Encryption for Secure Computation on Big Data. In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - SPBDIoT; ISBN 978-989-758-296-7; ISSN 2184-4976, SciTePress, pages 340-347. DOI: 10.5220/0006823203400347

@conference{spbdiot18,
author={Roger A. Hallman. and Mamadou H. Diallo. and Michael A. August. and Christopher T. Graves.},
title={Homomorphic Encryption for Secure Computation on Big Data},
booktitle={Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - SPBDIoT},
year={2018},
pages={340-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006823203400347},
isbn={978-989-758-296-7},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - SPBDIoT
TI - Homomorphic Encryption for Secure Computation on Big Data
SN - 978-989-758-296-7
IS - 2184-4976
AU - Hallman, R.
AU - Diallo, M.
AU - August, M.
AU - Graves, C.
PY - 2018
SP - 340
EP - 347
DO - 10.5220/0006823203400347
PB - SciTePress

- Science and Technology Publications, Lda.
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