Page 237 - 《软件学报》2021年第11期
P. 237

软件学报 ISSN 1000-9825, CODEN RUXUEW                                       E-mail: jos@iscas.ac.cn
                 Journal of Software,2021,32(11):3563−3575 [doi: 10.13328/j.cnki.jos.006073]   http://www.jos.org.cn
                 ©中国科学院软件研究所版权所有.                                                         Tel: +86-10-62562563


                                                                 ∗
                 基于阈值动态调整的重复数据删除方案

                                        1,3
                                1
                 咸鹤群  1,2,3 ,   高   原 ,   穆雪莲 ,   高文静  1
                 1
                 (青岛大学  计算机科学技术学院,山东  青岛  266071)
                 2
                 (信息安全国家重点实验室(中国科学院  信息工程研究所),北京   100093)
                 3 (广西密码学与信息安全重点实验室(桂林电子科技大学),广西  桂林  541004)
                 通讯作者:  咸鹤群, E-mail: xianhq@126.com

                 摘   要:  云存储已经成为一种主流应用模式.随着用户及存储数据量的增加,云存储提供商采用重复数据删除技
                 术来节省存储空间和资源.现有方案普遍采用统一的流行度阈值对所有数据进行删重处理,没有考虑到不同的数据
                 信息具有不同的隐私程度这一实际问题.提出了一种基于阈值动态调整的重复数据删除方案,确保了上传数据及相
                 关操作的安全性.提出了理想阈值的概念,消除了传统方案中为所有数据分配统一阈值所带来的弊端.使用项目反应
                 理论确定不同数据的敏感性及其隐私分数,保证了数据隐私分数的适用性,解决了部分用户忽视隐私的问题.提出了
                 基于数据加密的隐私分数查询反馈机制,在此基础上,设计了流行度阈值随数据上传的动态调整方法.实验数据及对
                 比分析结果表明,基于阈值动态调整的重复数据删除方案具有良好的可扩展性和实用性.
                 关键词:  重复数据删除;项目反应理论;阈值动态调整;理想阈值
                 中图法分类号: TP311

                 中文引用格式:  咸鹤群,高原,穆雪莲,高文静.基于阈值动态调整的重复数据删除方案.软件学报,2021,32(11):3563−3575.
                 http://www.jos.org.cn/1000-9825/6073.htm
                 英文引用格式: Xian HQ, Gao Y, Mu XL, Gao WJ. Deduplication scheme based on threshold dynamic adjustment. Ruan Jian Xue
                 Bao/Journal of Software, 2021,32(11):3563−3575 (in Chinese). http://www.jos.org.cn/1000-9825/6073.htm

                 Deduplication Scheme Based on Threshold Dynamic Adjustment
                                                      1,3
                                         1
                 XIAN He-Qun 1,2,3 ,   GAO Yuan ,   MU Xue-Lian ,   GAO Wen-Jing 1
                 1 (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)
                 2 (State Key Laboratory of Information Security (Institute of Information Engineering, Chinese Academy of Sciences), Beijing 100093,
                  China)
                 3 (Guangxi Key laboratory of Cryptography and Information Security (Guilin University of Electronic Technology), Guilin 541004, China)

                 Abstract:    Cloud  storage  has  become a  major application model. As the  number  of users and data  volume  increase, cloud  storage
                 providers use deduplication technology  to reserve  storage  space  and resources.  Existing solutions generally  use  a uniform popularity
                 threshold to process all the data, while the issue is not addressed that different data information should have different privacy levels. A
                 deduplication scheme is proposed based on threshold dynamic adjustment to ensure the security of uploaded data and related operations.
                 The concept of ideal threshold is introduced, which can be used to eliminate the drawbacks of uniform threshold in the traditional schemes.
                 The item response theory is adopted to determine the sensitivity of different data and their privacy scores, which ensures the applicability
                 of data privacy scores, it  can solve  the problem  that  some users  care little  about privacy  issues.  A privacy score query  and response
                 mechanism are proposed based on data encryption. On this basis, the dynamic adjustment method of the popularity threshold is designed

                   ∗  基金项目:  国家自然科学基金(61702294);  山东省自然科学基金(ZR2019MF058);  信息安全国家重点实验室开放课题(2020-
                 MS-09)
                     Foundation  item:  National  Natural Science Foundation of  China (61702294); Natural Science Foundation of  Shandong Province
                 (ZR2019MF058); Open Project of State Key Laboratory of Information Security (2020-MS-09)
                     收稿时间: 2018-12-08;  修改时间: 2019-10-08;  采用时间: 2020-04-29
   232   233   234   235   236   237   238   239   240   241   242