VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain  

作　　者：Youlin Ji Bo Yin Ke Gu 

机构地区：[1]School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha,410114,China

出　　处：《Computers, Materials & Continua》2024年第3期4205-4223,共19页计算机、材料和连续体（英文）

摘　　要：With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

关 键 词：SECURITY data sharing blockchain data query privacy protection 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP309[自动化与计算机技术—计算机科学与技术]