大数据环境下的用户隐私保护与系统风险控制的融合问题研究

批准号:
61972350
项目类别:
面上项目
资助金额:
45.0 万元
负责人:
练斌
依托单位:
学科分类:
网络与系统安全
结题年份:
2023
批准年份:
2019
项目状态:
已结题
项目参与者:
练斌
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中文摘要
大数据应用需要保护用户隐私,这要求匿名和行为不可链接,由此带来违规登录、身份克隆、甚至攻击系统的风险。.针对违规登录控制,提出新密码模块“知识泄漏的零知识证明”。通过剖析其数学本质,对经典零知识证明的内部结构进行重新设计,融合知识泄漏和零知识属性,并完成系列理论问题研究。与现有解决思路相比,计算复杂度从O(k)降为O(1),效率提升1~2个数量级。.针对用户身份克隆,提出“行为隐性链接—制约”匿名认证协议。通过基本的数学构造,解决基础密码难题,设计新型可验证随机函数,探索安全协议证明新理论。首次提出无用户登录限制、无系统周期规定的实用方案,与现有解决思路相比,效率提升2~3个数量级。.本项目实现隐私保护和违规控制的统一,无需特殊硬件和环境安全假设,适合普遍的信息系统,彻底解决利用匿名属性攻击大数据系统的问题。另外,为未来区块链系统可能出现的用户违规问题提供解决方案。
英文摘要
Big data applications need to protect user's privacy, which requires the anonymity and the unlinkability of behavior, thus it brings the risk of illegal login, identity cloning, and even attacking the system..Aiming at the control of illegal login, a new cryptography module named "zero-knowledge proof with knowledge leakage" is proposed. By analyzing its mathematical essence, the internal structure of the classical zero-knowledge proof will be redesigned, the knowledge leakage and zero-knowledge attribute will be integrated, and a series of theoretical research will be completed. Compared with the existing solutions, the computational complexity is reduced from O(k) to O(1), and the efficiency is improved by 1-2 orders of magnitude..Aiming at user’s identity cloning, an anonymous authentication protocol of "invisible behavior linkability - restriction" is proposed. Constructing the basic mathematical units, we will solve the basic cryptographic problems, design new verifiable random functions and explore the new proof theory of security protocol. For the first time, a practical scheme without user login restriction and system period stipulation is proposed. Compared with the existing solutions, the efficiency is improved by 2~3 orders of magnitude..This project realizes the unification of privacy protection and violation control. Without special hardware and any environment security assumptions, it is applicable to common information systems. Moreover, the problem of abusing anonymity to attack big data system is thoroughly solved. In addition, it provides solutions to the possible user violation in the future blockchain system.
期刊论文列表
专著列表
科研奖励列表
会议论文列表
专利列表
DOI:10.1109/access.2023.3264497
发表时间:2023
期刊:IEEE Access
影响因子:3.9
作者:Xiaodan Jin;Fuqun Wang;Renjun Zhang;Bin Lian;Kefei Chen
通讯作者:Xiaodan Jin;Fuqun Wang;Renjun Zhang;Bin Lian;Kefei Chen
DOI:10.3390/su15075656
发表时间:2023-04-01
期刊:SUSTAINABILITY
影响因子:3.9
作者:Zheng,Wei;Wen,Shiting;Nie,Ya
通讯作者:Nie,Ya
DOI:10.1109/tcsii.2022.3197314
发表时间:2022-12
期刊:IEEE Transactions on Circuits and Systems II: Express Briefs
影响因子:--
作者:Ruoyu Wu;Ming Xu;Yingqing Yang;Guanzhong Tian;Ping Yu;Yang Zhao;Bin Lian;Longhua Ma
通讯作者:Ruoyu Wu;Ming Xu;Yingqing Yang;Guanzhong Tian;Ping Yu;Yang Zhao;Bin Lian;Longhua Ma
DOI:10.1142/S0218126624502050
发表时间:2024
期刊:Journal of Circuits, Systems, and Computers
影响因子:--
作者:Xianghong Zhao;Longhua Ma;Weiming Cai;Bin Lian;Jialin Cui;Lingjian Ye
通讯作者:Lingjian Ye
DOI:10.3390/bios12121087
发表时间:2022-11-28
期刊:BIOSENSORS-BASEL
影响因子:5.4
作者:Wu, Wei;Ma, Longhua;Lian, Bin;Cai, Weiming;Zhao, Xianghong
通讯作者:Zhao, Xianghong
防疫数据的可信采集及跨域应用的安全技术研究
- 批准号:LY23F020013
- 项目类别:省市级项目
- 资助金额:0.0万元
- 批准年份:2023
- 负责人:练斌
- 依托单位:
国内基金
海外基金
