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

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.

scholarly journals Two-Party Secure Computation for Any Polynomial Function on Ciphertexts under Different Secret Keys

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Bingbing Jiang
Keyword(s):  
Polynomial Function ◽  
Homomorphic Encryption ◽  
Auxiliary Information ◽  
Secure Computation ◽  
Encryption Algorithm ◽  
Fully Homomorphic Encryption ◽  
Simple Scheme ◽  
Secret Keys ◽  
Learning With Errors ◽  
Multiple Keys

Multikey fully homomorphic encryption proposed by Lopez-Alt et al. (STOC12) is a significant primitive that allows one to perform computation on the ciphertexts encrypted by multiple different keys independently. Then, several schemes were constructed based on decisional small polynomial ratio or learning with errors. These schemes all require an expansion algorithm to transform a ciphertext under a single key into an encryption of the same message under a set of keys. To achieve the expansion algorithm without interaction with these key-keepers, their encryption algorithm not only outputs a ciphertext of a plaintext but also exports auxiliary information generated from the randomness used in the former encryption process. Beyond that, the size of the ciphertext encrypted by multiple keys increases linearly or quadratically in the number of participants. In this paper, we studied the problem whether someone can directly perform arbitrary computation on ciphertexts encrypted by different keys without any auxiliary information in the output of the encryption algorithm and an increase in the size of the ciphertext in the expansion algorithm. To this end, we proposed a novel and simple scheme of secure computation on ciphertexts under two different keys directly without any auxiliary information. In other words, each party just provides its own ciphertexts encrypted by the GSW scheme (CRYPTO13). In the procedure of executing evaluation on these ciphertexts, the size of the new ciphertext remains the same as that of the GSW ciphertext.

scholarly journals A Modified Symmetric Key Fully Homomorphic Encryption Scheme Based on Read-Muller Code

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0899
Author(s):  
RatnaKumari Challa ◽  
VijayaKumari Gunta
Keyword(s):  
Coding Theory ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Chosen Plaintext Attack ◽  
Security Proof ◽  
Expansion Technique ◽  
Cryptographic Primitive ◽  
Symmetric Key

Homomorphic encryption became popular and powerful cryptographic primitive for various cloud computing applications. In the recent decades several developments has been made. Few schemes based on coding theory have been proposed but none of them support unlimited operations with security.   We propose a modified Reed-Muller Code based symmetric key fully homomorphic encryption to improve its security by using message expansion technique. Message expansion with prepended random fixed length string provides one-to-many mapping between message and codeword, thus one-to many mapping between plaintext and ciphertext. The proposed scheme supports both (MOD 2) additive and multiplication operations unlimitedly.   We make an effort to prove the security of the scheme under indistinguishability under chosen-plaintext attack (IND-CPA) through a game-based security proof. The security proof gives a mathematical analysis and its complexity of hardness. Also, it presents security analysis against all the known attacks with respect to the message expansion and homomorphic operations.

Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree

2013 ◽  
pp. 82-102
Author(s):  
Golam Kaosar ◽  
Xun Yi
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Secure Computation ◽  
Rule Mining ◽  
Fully Homomorphic Encryption ◽  
Popular Method ◽  
Tree Method

Frequent Path tree (FP-tree) is a popular method to compute association rules and is faster than Apriori-based solutions in some cases. Association rule mining using FP-tree method cannot ensure entire privacy since frequency of the itemsets are required to share among participants at the first stage. Moreover, FP-tree method requires two scans of database transactions which may not be the best solution if the database is very large or the database server does not allow multiple scans. In addition, one-pass FP-tree can accommodate continuous or periodically changing databases without restarting the process as opposed to a regular FP-tree based solution. In this paper, the authors propose a one-pass FP-tree method to perform association rule mining without compromising any data privacy among two parties. A fully homomorphic encryption system over integer numbers is applied to ensure secure computation among two data sites without disclosing any number belongs to themselves.

scholarly journals SVAT: Secure Outsourcing of Variant Annotation and Genotype Aggregation

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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