Delegatable Homomorphic Encryption with Applications to Secure Outsourcing of Computation

2020 ◽

Vol 9 (5) ◽

pp. 545-550

Keyword(s):

Cloud Computing ◽

Distributed Computing ◽

Data Storage ◽

Large Scale ◽

Homomorphic Encryption ◽

Linear Equations ◽

Force Transmission ◽

Large Scale Systems ◽

Secure Outsourcing ◽

Systems Of Linear Equations

Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of registering model for clients when their secret information are expended during the figuring procedure. Critical thinking is a system to arrive at the pragmatic objective of specific instruments that tackles the issues as well as shield from pernicious practices.. In this paper, we examine secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. Linear programming is an operation research technique formulates private data by the customer for LP problem as a set of matrices and vectors, to develop a set of efficient privacypreserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output information. Identify that LP problem solving in Cloud component is efficient extra cost on cloud server. In this paper we are utilizing Homomorphic encryption system to increase the performance and time efficiency

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Blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice

Parallel and Distributed Computing, Applications and Technologies - Lecture Notes in Computer Science ◽

10.1007/978-3-030-69244-5_13 ◽

2021 ◽

pp. 154-165

Author(s):

Mingyang Song ◽

Yingpeng Sang ◽

Yuying Zeng ◽

Shunchao Luo

Keyword(s):

Homomorphic Encryption ◽

Ideal Lattice ◽

Fully Homomorphic Encryption ◽

Secure Outsourcing

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Blockchain-Based Secure Outsourcing of Polynomial Multiplication and Its Application in Fully Homomorphic Encryption

Security and Communication Networks ◽

10.1155/2021/9962575 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Mingyang Song ◽

Yingpeng Sang ◽

Yuying Zeng ◽

Shunchao Luo

Keyword(s):

Homomorphic Encryption ◽

Security Analysis ◽

Secure Computation ◽

Modular Exponentiation ◽

Local Computation ◽

Fully Homomorphic Encryption ◽

Polynomial Multiplication ◽

Secure Outsourcing ◽

Cheating Detection ◽

Constrained Devices

The efficiency of fully homomorphic encryption has always affected its practicality. With the dawn of Internet of things, the demand for computation and encryption on resource-constrained devices is increasing. Complex cryptographic computing is a major burden for those devices, while outsourcing can provide great convenience for them. In this paper, we firstly propose a generic blockchain-based framework for secure computation outsourcing and then propose an algorithm for secure outsourcing of polynomial multiplication into the blockchain. Our algorithm for polynomial multiplication can reduce the local computation cost to O n . Previous work based on Fast Fourier Transform can only achieve O n log n for the local cost. Finally, we integrate the two secure outsourcing schemes for polynomial multiplication and modular exponentiation into the fully homomorphic encryption using hidden ideal lattice and get an outsourcing scheme of fully homomorphic encryption. Through security analysis, our schemes achieve the goals of privacy protection against passive attackers and cheating detection against active attackers. Experiments also demonstrate our schemes are more efficient in comparisons with the corresponding nonoutsourcing schemes.

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Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Cell Systems ◽

10.1016/j.cels.2021.07.010 ◽

2021 ◽

Author(s):

Miran Kim ◽

Arif Ozgun Harmanci ◽

Jean-Philippe Bossuat ◽

Sergiu Carpov ◽

Jung Hee Cheon ◽

...

Keyword(s):

Homomorphic Encryption ◽

Genotype Imputation ◽

Secure Outsourcing

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SVAT: Secure Outsourcing of Variant Annotation and Genotype Aggregation

10.1101/2021.09.28.462259 ◽

2021 ◽

Author(s):

Miran Kim ◽

Su Wang ◽

Xiaoqian Jiang ◽

Arif Ozgun Harmanci

Keyword(s):

Genetic Variants ◽

Rare Variants ◽

Homomorphic Encryption ◽

Data Representation ◽

Secure Computation ◽

Allele Frequencies ◽

Indigenous Populations ◽

Genetic Privacy ◽

Variant Annotation ◽

Secure Outsourcing

Background: Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight for genetic determinants of diseases. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions. Results: We present SVAT, a method for secure outsourcing of variant annotation and aggregation, which are two basic steps in variant interpretation and detection of causal variants. SVAT uses homomorphic encryption to encrypt the data at the client-side. The data always stays encrypted while it is stored, in-transit, and most importantly while it is analyzed. SVAT makes use of a vectorized data representation to convert annotation and aggregation into efficient vectorized operations in a single framework. Also, SVAT utilizes a secure re-encryption approach so that multiple disparate genotype datasets can be combined for federated aggregation and secure computation of allele frequencies on the aggregated dataset. Conclusions: Overall, SVAT provides a secure, flexible, and practical framework for privacy-aware outsourcing of annotation, filtering, and aggregation of genetic variants. SVAT is publicly available for download from https://github.com/harmancilab/SVAT .

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