移动云计算的可靠推荐与隐私保护跨层信誉机制

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"该文是关于‘基于可靠推荐和隐私保护的跨层信誉的移动云计算机制’的研究论文，发表在《Future Generation Computer Systems》杂志上，由Hui Lin、Li Xu、Yi Mu和Wei Wu合作撰写。文章提出了一个名为RP-CRM（Recommendation and Privacy-preserving based Cross-layer Reputation Mechanism）的新机制，旨在提高移动云计算的安全性、隐私保护能力和效率，同时能够快速准确地检测和管理恶意节点。RP-CRM的表现优于SLCRM（Single Layer Cloud Reputation Mechanism）和FSLR（Fuzzy System-based Local Reputation）。" 移动云计算是一种将计算和存储能力从本地设备扩展到云环境的技术，它为用户提供高效、便捷的服务。然而，随着移动云计算的普及，安全性和用户隐私成为亟待解决的问题。本文提出的RP-CRM机制是为了解决这些问题而设计的。 RP-CRM机制的核心是结合了可靠推荐和隐私保护策略的跨层信誉模型。在这个模型中，"可靠推荐"是指通过分析用户和服务的历史行为，生成对服务的信任度评估，以帮助用户选择可信赖的服务。这有助于防止恶意服务的欺骗行为，提高服务的可靠性。另一方面，"隐私保护"是通过设计安全的数据传输和存储方法，确保用户数据不被未经授权的第三方访问或滥用，保护用户的个人信息安全。文章中提到，RP-CRM具有快速响应和高精度的特性，能有效检测和管理网络中的恶意节点，降低由恶意节点引起的攻击风险。与SLCRM和FSLR相比，RP-CRM在推荐效果和服务接受率方面表现出优越性能，这意味着RP-CRM更能够为用户提供满意的服务选择，同时保持较高的系统稳定性。此外，RP-CRM的成功接受率更高，表明该机制在实际应用中能够获得更高的用户满意度。这反映了RP-CRM在移动云计算环境下，兼顾了服务质量和用户隐私的平衡，从而提高了整个系统的整体性能。这篇研究论文为移动云计算的安全性和用户隐私提供了一个创新的解决方案。RP-CRM机制通过整合可靠推荐和隐私保护策略，提升了跨层信誉管理的效能，为未来移动云计算环境的设计和优化提供了重要的理论和技术支持。

H. Lin et al. / Future Generation Computer Systems 52 (2015) 125–136 127

increasing their reputation then consider it when selecting multi-

point relay nodes. In addition, the model also detects nodes behav-

ing selfishly after selection according to the final decision of the

detection basing on a weighted decision where the most trusted

node has the leading contribution.

Singh et al. [13] presented a trust evaluation framework to

calculate trust on service providers in a cloud computing environ-

ment. The model takes into consideration three views to calcu-

late final trusts which are consumers self trust on service provider,

friends trust on service provider and third party’s trust on ser-

vice provider. Kim et al. [9] proposed a trust management mecha-

nism for reliable data integration, management and applications

in MCC. The mechanism suggested a method to quantify a one-

dimensional trusting relation based on the analysis of telephone

call data from mobile devices. And then, it integrated the inter-

user trust relationship in MCC. Hammam et al. [14] proposed a

trust management system (TMC) for mobile ad-hoc clouds to ver-

ify that participants are reliable, available, and not malicious. The

TMC considered availability, neighbors evaluation and response

quality and task completeness and calculated the reputation trust

value for nodes through EigenTrust algorithm. Chen et al. [10] pro-

posed a global social trust model building system in MCC to pro-

vide trustworthy communicating path and helps address security

issues with increased trustworthiness of user behaviors. The model

described the implicit call behavioral graph formed by users inter-

actions with call and rated these relationships to form a dynamic

local cloud trust. And then, it discussed the propagation and aggre-

gation of local trust values for global social cloud network.

All existing reputation mechanisms or trust models are based

on the direct observation of layer-specifics to evaluate the node

reputation, thus ignoring many key factors of reputation in other

layers such as node forwarding behaviors at the network layer,

channel collisions at the MAC layer and channel quality measures

at the physical layer. Also, none of them take into account the

malicious behavior of the recommendation nodes within the

recommendation reputation evaluation. In addition, none of them

pay attention to the reputation with privacy as a main concern.

Therefore, it is still an open problem and a challenging task

to design a cross-layer and privacy preserving based efficient

reputation mechanism for WM-MCC to defend against the internal

multi-layer, bad mouthing and information disclosure attacks.

3. Reliable recommendation and privacy-preserving based

cross-layer reputation mechanism RP-CRM

In this section, a novel reliable recommendation and privacy

preserving based cross-layer reputation mechanism named RP-

CRM for WM-MCC is extended from our previous work [21–23].

The structure of RP-CRM is described in Fig. 2, which includes three

components: cross-layer reputation fusion center (CRFC), reputa-

tion databases (RD) and reputation computation module (RCM).

The RCM is responsible for the direct reputation computation on

different layers and stores the results into the corresponding RD.

Then, the RD provides direct reputation to the specific layer fi-

nal reputation fusion (SFRF) module in CRFC to compute the spe-

cific layer final reputation and provides the value to the cross-layer

final reputation fusion (CFRF) module in CRFC. Finally, the CFRF ex-

ecutes the cross-layer final reputation fusion according to the spe-

cific requirements from the different layers.

The RP-CRM consists of four phases: reputation query, specific

layer reputation computation and update, cross-layer final reputa-

tion computation, and malicious node classification and manage-

ment.

Fig. 2. The structure of RP-CRM.

3.1. Reputation query

The reputation query phase is to query the request and reply

node reputation through direct or recommended way.

When node x wants to select a neighbor node to provide ser-

vice such as relay, it will send a request message to the neigh-

bor nodes. Each neighbor node such as y receiving the request will

first execute the requester identification function as shown in Al-

gorithm 1 to evaluate x’s reputation and judges whether it is a ma-

licious node. If the requester identification function cannot make

a decision, y will execute the recommendation reputation query

function described in Algorithm 2 to query x’s reputation from its

neighbor nodes. Once receiving the reply messages from its neigh-

bors, y will start the recommendation reputation computation and

the final reputation computation processes in Sections 3.2.2, 3.2.3

and 3.3. If x is considered to be a malicious node, it will be punished

or isolated according to the malicious node classification and man-

agement scheme in Section 3.4. Otherwise, y will send x an accept

message.

After receiving the affirmative response from its neighbors, x

will also execute the above mentioned query process to evaluate

the repliers reputation and decide whether to select or select which

nodes as the service providers.

Algorithm 1: Requester Identification

Input: Requester x’s mac address, sl and sc

Output: x is a malicious node or not

begin

Requester x sends a request(mac, sl, sc, broadcast, TTL = 1);

x’s neighbor nodes such as y receives the request message;

If (x’ssl > TH

down

) ∧ (x’ssc > TH

down

) then

y executes the Direct Reputation Computation and returns

the result as:

Direct

= Direct_reputation (mac, sl, sc);

Else

y drops the request message;

If (R

Direct

> TH

upper

direct

) then

Final

= R

Direct

;

Else If (TH

down

direct

< R

Direct

< TH

upper

direct

) then

y executes the Recommendation Reputation Query Function;

y executes the Recommendation Reputation Computation;

y executes the Final Reputation Computation and gets the R

Final

;

Else

Final

= −1;

If (R

Final

< TH

down

final

) then

x is considered as a malicious node and will be isolated;

Else If (TH

down

final

< R

Final

< TH

upper

final

) then

x will be punished by decreasing its reputation value;

Else

x is considered as a trustworthy node;

y sends accept(x, y’s mac, y’s sl, y’s sc)tox;

end

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