云环境中数据安全去重研究进展

doi:10.11959/j.issn.1000-436x.2016238

摘要/Abstract

摘要：

为了提高云存储效率和节约网络通信带宽，需要对云端同一数据的多个副本执行重复性检测与去重操作，而云环境下的密文数据阻碍了数据安全去重的实施，这一问题迅速引起学术界和产业界的广泛关注，成为研究热点。从安全性角度出发，分析云环境中数据安全去重的原因及面临的主要挑战，给出云数据安全去重的系统模型和威胁模型，面向云数据安全去重技术的实现机制从基于内容加密的安全去重、基于所有权证明的安全去重和隐私保护的安全去重3个方面对近年来相关研究工作进行深入分析和评述，并指出各种关键技术与方法的优势及存在的共性问题；最后给出云数据安全去重领域未来的研究方向与发展趋势。

关键词: 安全去重, 基于内容的加密, 所有权证明, 隐私保护, 重复数据删除

Abstract:

In order to improve the efficiency of cloud storage and save the communication bandwidth, a deduplication mechanism for multi-duplicate of the same data in cloud environment was needed. However, the implement of the secure data deduplication was seriously hindered by the ciphertext in cloud. This issue has quickly aroused wide attention of academia and industry, and became a research hotspot. From a security standpoint, firstly the primary cause and the main challenges of secure data deduplication in cloud environment was analyzed, and then the deduplication system model as well as its security model was described. Furthermore, focusing on the realization mechanism of secure data deduplica-tion, the thorough analyses were carried on and reviews for the related research works in recent years from content-based encryption, proof of ownership and privacy protection for secure deduplication, then the advantages and common prob-lems of various key technologies and methods were summed up. Finally, the future research directions and development trends on secure data deduplication in cloud was given.

Key words: secure deduplication, content-based encryption, proof of ownership, privacy protection, data deduplication

熊金波,张媛媛,李凤华,李素萍,任君,姚志强. 云环境中数据安全去重研究进展[J]. 通信学报, 2016, 37(11): 169-180.

Jin-bo XIONG,Yuan-yuan ZHANG,Feng-hua LI,Su-ping LI,Jun REN,Zhi-qiang YAO. Research progress on secure data deduplication in cloud[J]. Journal on Communications, 2016, 37(11): 169-180.

图/表 8

图1

表1

图2

表2

表3

表4

经典基于PoW的去重方案的开销对比分析"

方案	客户端计算开销	客户端读写开销	服务器初始化计算开销	服务器去重计算开销	服务器初始化读写开销	服务器常规读写开销	服务器内存开销	通信带宽消耗
PoW^[23]	O(f)Hash	O(f)	O(f) Hash	O(1)	O(f)	O(0)	O(1)	O(λlogλ)
s-PoW^[26]	O(f)Hash	O(f)	O(f) Hash	O(nλ)PRF	O(f)	O(nλ)	O(nλ)	O(λ)
BF-PoW^[27]	O(f)Hash	O(f)	O(f) Hash	O(?)Hash	O(f)	O(0)	O(Ψ)	$O (\frac{1 λ}{p_{f}})$
CE-PoW^[28]	O(b) CE·Hash·Hash	O(f)	O(b)·Hash·Hash	O(nlλ)PRNG	O(f)	O(0)	O(nlλ)	O(lλ)
注：其中，f表示文件长度，n表示预设的挑战数目，λ表示安全参数，p_f表示BF的误判率，l表示token的长度，Hash表示执行一次散列函数的开销，CE表示执行一次收敛加密的开销，PRF表示执行一次伪随机函数的开销，PRNG(pseudo-random number generator)表示执行一次伪随机序列发生器的开销， $Ω = \frac{1 λ (\log \frac{1}{p_{f}})}{p_{f}}$ , $ψ = \frac{\log \frac{1}{p_{f}}}{l}$ 。

表4

表5

表6

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攻击类型	抵抗攻击的方案
蛮力攻击	DupLESS^[14]、REED^[15]、multi-server-aided^[17]
侧信道攻击	CDStore^[10]、ClouDedup^[16]、Shin方案^[22]
字典攻击	DupLESS^[14]、ClouDedup^[16]、PerfectDedup^[19]
伪造攻击	MLE^[11]、iMLE^[13]
Sybil攻击	Stanek方案^[18]

方案	采用的主要算法	抗攻击类型	第三方服务器	数据去重级别	谁主导去重实施
CE^[9]	CE	—	无	文件级去重	客户端
CDStore^[10]	CE+AONT-RS	侧信道攻击	无	块级去重	客户端
PerfectDedup^[19]	CE+PHF	目录攻击	索引服务器	块级去重	客户端
ClouDedup^[16]	CE+访问控制策略	目录攻击，侧信道攻击	密钥服务器	块级去重	跨用户
Stanek^[18]	门限+CE	Sybil攻击	索引、身份验证服务器	文件级去重	跨用户
MLE^[11]	MLE	—	无	文件级去重	客户端
DupLESS^[14]	MLE	蛮力攻击	密钥服务器	文件级去重	客户端
BL-MLE^[12]	BL-MLE+PoW	文件分发攻击	无	块级去重	客户端
multi-server-aided^[17]	门限盲签名+可校验秘密共享	蛮力攻击	多密钥服务器	文件级去重	客户端
iMLE^[13]	iMLE	伪造攻击	无	文件级去重	跨用户
REED^[15]	MLE+AONT-RS	蛮力攻击	密钥服务器	块级去重	客户端

方案	客户端开销	服务器端开销	初始通信带宽消耗	常规通信带宽消耗
CE^[9]	O(f)Hash	O(f)	O(f)	O(g)
MLE^[11]	O(f)Hash·Hash	O(f)	O(f)	O(g)
DupLESS^[14]	O(f)Hash·Hash	O(f)OPRF	O(f)	O(g)
BL-MLE^[12]	O(b)Hash·Hash	O(b) PoW	O(f)	O(gλ)
iMLE^[13]	O(f)Hash	O(fp)	O(f)	O(gp)
注：f表示文件的长度，b表示文件块的长度，p表示系统参数，λ表示安全参数，g表示文件指纹的长度，Hash表示执行一次散列函数的开销， PoW表示执行一次所有权证明的开销，OPRF表示执行一次不经意伪随机函数的开销。

隐私保护方案	隐私保护程度	数据缺损	数据依赖性	通信带宽消耗	服务器内存消耗
Harnik方案^[40]	低	低	低	O(i)	O(if)
Lee方案^[41]	中	低	低	O(i)	O(if)
Shin方案^[22]	高	高	高	O(i)	O(f)
注：i代表上传文件的次数，f代表文件F的长度。

分类	相关技术	典型方案	主要算法	优点	局限性
基于内容加密的安全去重	基于 CE 实现云数据安全去重基于 MLE 实现云数据安全去重	CE^[9]BL-MLE^[12]	CEBL-MLE+PoW	密文的重复性检测明确安全目标和形式化定义，块级去重	不能达到语义安全，易遭受蛮力攻击计算开销较大，遭受蛮力攻击
	基于MHT的PoW	Halevi^[23]方案	MHT	提出PoW概念，抵抗侧信道攻击	计算开销较大，未考虑敏感数据的加密保护
基于PoW的安全去重	基于随机抽样的PoW	CE-PoW^[28]	CE+随机抽样	减少计算开销，高效，无可信第三方	不能实现语义安全，易导致内容猜测攻击
	基于广义散列函数的PoW	Yang ^[31]方案	PoF+抽样检测+动态系数	根据完整源文件的所有权证明	计算开销较大，遭受蛮力攻击
基于隐私保护的安全去重	基于随机化方法的隐私保护安全去重基于差分隐私的隐私保护安全去重	Harnik^[40]方案Lee ^[41]方案Shin^[22]方案	随机化方法差分隐私	数据真实无缺损，减少去重中的隐私泄露的概率隐私保护程度较高，可抵抗侧信道攻击及关联文件攻击	无法抵抗关联文件攻击，易遭受蛮力攻击添加噪声容易导致数据失真，数据依赖性高，需要根据不同的数据计算所添加噪声大小