Privacy Computing in Environmental Big Data on Blockchain

doi:10.3969/j.issn.1671-1122.2024.10.005

Abstract

Abstract:

In recent years, with the continuous introduction of policies related to security, healthcare and environmental protection in China, the value of environmental data is increasingly important. However, the scientific management and secure sharing of environmental data in China is still in its infancy, and the amount of environmental data with privacy protection needs has increased a lot. However, there are many problems in data sharing, such as isolation of data centers and high risk of privacy leakage. Aiming at the environmental data sharing scenario and satisfying the privacy protection needs of the users’ data cloud storage and cloud computing, the article combine blockchain with privacy computing to construct a data security management system based on fully homomorphic encryption and searchable encryption by using the national cryptography algorithms. Relying on blockchain deployment, cloud service storage and computing support, the system can realize two functions: machine learning supported by fully homomorphic encryption and searchable encryption. Based on a fully homomorphic encryption scheme, this article implemented a neural network prediction model and completed privacy computing on encrypted data. Moreover, this article realized a symmetric searchable encryption scheme, and the encrypted data can be stored in the cloud during the whole process, achieving ciphertext retrieval while protecting the privacy of query keywords. This system is deployed on the EOS blockchain with a weakly centralized third party, where the blockchain provides a reliable platform for data transactions and digital evidences, and the IPFS(Inter Planetary File System) provides a safe custody platform for encrypted data storage. As a result, the data circulation channels of all participants are effectively connected while ensuring both privacy and availability of encrypted data.

Key words: fully homomorphic encryption, symmetric searchable encryption, blockchain, machine learning, privacy computing

CLC Number:

TP309

WANG Nan, YUAN Ye, YANG Haoran, WEN Zhouzhi, SU Ming, LIU Xiaoguang. Privacy Computing in Environmental Big Data on Blockchain[J]. Netinfo Security, 2024, 24(10): 1515-1527.

Figures/Tables 14

References 30

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参数	含义
xⁿ+1	模多项式
q	密文系数模数
t	明文系数模数
$R_{t}^{n},R_{q}^{n}$	明文和密文空间多项式环
m	明文消息
c	密文消息
sk	私钥
pk	公钥
e	噪声
u	掩码

参数	含义
doc_i	设备i的未加密数据文档
Doc_i	设备i的CBC-SM4加密的数据文档
ek_i	设备i的加密密钥
fk_i	设备i的查询主密钥
w_{i, j}	文档doc_i的第j个关键词
${{T}_{{{w}_{i,j}}}}$	关键词w_{i, j}的陷门
BF_i	设备i的布隆过滤器索引向量
L_i	文档doc_i的关键词总个数
N	采集数据的设备总数
s, r, δ	安全参数

多项式次数	多项式函数表达式
2	y₂=0.09401488x²+0.49922105x+0.46555915
3	y₃=0.000118167887x³+0.0940148772x²+0.497444982x+0.46555915
4	y₄=-0.00324432116x⁴+0.000118167487x³+0.163675005x²+0.497444995x+0.291060996
5	y₅=0.0000042x⁵-0.003244x⁴+0.00000122x³+0.163675005x²+0.498072743x+0.291060996
6	y₆=0.0002379x⁶+0.0000042x⁵-0.01137x⁴+0.00000122x³+0.2315x²+0.4981x+0.210103044

参数名称	具体数值
模多项式	x⁴⁰⁹⁶+1
密文系数模数	36028797014376449×36028797013327873（110 bit）
明文系数模数	4096

参数名称	具体数值
模多项式	x⁸¹⁹²+1
密文系数模数	8796092858369×8796092792833×17592186028033× 17592185438209×17592184717313（218 bit）
明文系数模数	1152921504606830593（60 bit）