Toward a Secure Drone System: Flying With Real-Time Homomorphic Authenticated Encryption

Cheon, Jung Hee; Han, Kyoung-Sik; Hong, Seong-Min; Kim, Hyoun Jin; Kim, Junsoo; Kim, Suseong; Seo, Hosung; Shim, Hyungbo; Song, Yongsoo

doi:10.1109/access.2018.2819189

Cited by 111 publications

(60 citation statements)

References 36 publications

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“…For privacy-preserving GWAS computation, we applied an HE scheme called HEAAN proposed by Cheon et al [3,4], which supports approximate computation of real numbers in encrypted state. Efficiency of HEAAN in the real world has been proved by showing its application in various fields including machine learning [8,22,23] and cyber physical system [24]. The winning solution of iDASH competition in 2017 also applied HEAAN as an HE scheme for privacy-preserving logistic regression on genomic data.…”

Section: Approximate Homomorphic Encryption Scheme Heaanmentioning

confidence: 99%

Privacy-preserving approximate GWAS computation based on homomorphic encryption

et al. 2020

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Background: One of three tasks in a secure genome analysis competition called iDASH 2018 was to develop a solution for privacy-preserving GWAS computation based on homomorphic encryption. The scenario is that a data holder encrypts a number of individual records, each of which consists of several phenotype and genotype data, and provide the encrypted data to an untrusted server. Then, the server performs a GWAS algorithm based on homomorphic encryption without the decryption key and outputs the result in encrypted state so that there is no information leakage on the sensitive data to the server. Methods: We develop a privacy-preserving semi-parallel GWAS algorithm by applying an approximate homomorphic encryption scheme HEAAN. Fisher scoring and semi-parallel GWAS algorithms are modified to be efficiently computed over homomorphically encrypted data with several optimization methodologies; substitute matrix inversion by an adjoint matrix, avoid computing a superfluous matrix of super-large size, and transform the algorithm into an approximate version. Results: Our modified semi-parallel GWAS algorithm based on homomorphic encryption which achieves 128-bit security takes 30-40 minutes for 245 samples containing 10,000-15,000 SNPs. Compared to the true p-value from the original semi-parallel GWAS algorithm, the F 1 score of our p-value result is over 0.99. Conclusions: Privacy-preserving semi-parallel GWAS computation can be efficiently done based on homomorphic encryption with sufficiently high accuracy compared to the semi-parallel GWAS computation in unencrypted state.

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Section: Approximate Homomorphic Encryption Scheme Heaanmentioning

confidence: 99%

Privacy-preserving approximate GWAS computation based on homomorphic encryption

et al. 2020

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“…At the expense of this bit accuracy, HEAAN supports very fast arithmetic operations on word-sized encrypted data. is fast computation speed allows HEAAN to be used in many fields such as machine learning and machine control [17,18]. In addition, unlike TFHE, HEAAN has the advantage of providing a complete SIMD function.…”

Section: Heaan Heaan's Ciphertext Can Contain Multiplementioning

confidence: 99%

An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

Moon

Lee

2020

Security and Communication Networks

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As a method of privacy-preserving data analysis (PPDA), a fully homomorphic encryption (FHE) has been in the spotlight recently. Unfortunately, because many data analysis methods assume that the type of data is of real type, the FHE-based PPDA methods could not support the enough level of accuracy due to the nature of FHE that fixed-point real-number representation is supported easily. In this paper, we propose a new method to represent encrypted floating-point real numbers on top of FHE. e proposed method is designed to have analogous range and accuracy to 32-bit floating-point number in IEEE 754 representation. We propose a method to perform arithmetic operations and size comparison operations. e proposed method is designed using two different FHEs, HEAAN and TFHE. As a result, HEAAN is proven to be very efficient for arithmetic operations and TFHE is efficient in size comparison. is study is expected to contribute to practical use of FHE-based PPDA.

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“…Chim et al [15] utilized Paillier homomorphic encryption and the bloom filters to proposed an aggregation scheme. Cheon et al [16] adopted homomorphic cryptography, which removes risks on the management of the secret key. Therefore, eliminating the requirement to encrypt and decrypt the message within the network.…”

Section: B Homomorphic Encryption Algorithmsmentioning

confidence: 99%

A Novel Energy-Efficient and Privacy-Preserving Data Aggregation for WSNs

Liu

Fang

et al. 2020

IEEE Access

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Data aggregation is a fundamental and efficient algorithm to reduce the communication overhead and energy consumption in wireless sensor networks (WSNs). However, WSNs need both energy-efficient and privacy-preserving when being deployed in an unprotected area. In this paper, we propose an energy-efficient and privacy-preserving data aggregation algorithm CBDA (the chain-based data aggregation). In the CBDA, sensor nodes will be organized as a tree topology. The leaf nodes of the tree sequentially reconnect with each other to form many chain topologies. For assuring data privacy, after data gathering, (1) the tail nodes (the nodes which on the tail of chain) need to slice their sensing data into J fragments. One fragment is kept by themselves, and they distribute the J −1 data fragments to their neighbor nodes. (2) Each tail node will inject some fake fragments into its J −1 fragments to interfere with adversaries. The CBDA can achieve less energy consumption and higher aggregation accuracy during data aggregation. We perform a comprehensive simulation to make a comparison among the CBDA with existing algorithms. The experimental results demonstrate that the CBDA outperforms the existing algorithms. INDEX TERMS Wireless sensor network, data processing, data privacy; energy conservation, accuracy.

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Toward a Secure Drone System: Flying With Real-Time Homomorphic Authenticated Encryption

Cited by 111 publications

References 36 publications

Privacy-preserving approximate GWAS computation based on homomorphic encryption

Privacy-preserving approximate GWAS computation based on homomorphic encryption

An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

A Novel Energy-Efficient and Privacy-Preserving Data Aggregation for WSNs

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