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      Searchable Encryption Scheme for Personalized Privacy in IoT-Based Big Data

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          Abstract

          The Internet of things (IoT) has become a significant part of our daily life. Composed of millions of intelligent devices, IoT can interconnect people with the physical world. With the development of IoT technology, the amount of data generated by sensors or devices is increasing dramatically. IoT-based big data has become a very active research area. One of the key issues in IoT-based big data is ensuring the utility of data while preserving privacy. In this paper, we deal with the protection of big data privacy in the data storage phase and propose a searchable encryption scheme satisfying personalized privacy needs. Our proposed scheme works for all file types including text, audio, image, video, etc., and meets different privacy needs of different individuals at the expense of high storage cost. We also show that our proposed scheme satisfies index indistinguishability and trapdoor indistinguishability.

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          Most cited references 26

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          Searchable symmetric encryption: Improved definitions and efficient constructions

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            Highly-Scalable Searchable Symmetric Encryption with Support for Boolean Queries

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              Algebraic feature extraction of image for recognition

               Zi-Quan Hong (1991)
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                01 March 2019
                March 2019
                : 19
                : 5
                Affiliations
                School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; lis198707@ 123456gmail.com (S.L.); lmiao1021@ 123456gmail.com (M.L.); xwzhouli@ 123456sina.com (X.Z.)
                Author notes
                [* ]Correspondence: xuhaitao@ 123456ustb.edu.cn ; Tel.: +86-10-6164-7796
                Article
                sensors-19-01059
                10.3390/s19051059
                6427167
                30832294
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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