P2P环境中基于影响的信息遗传信任模型
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摘要
P2P网络已成为Internet网络中重要的应用之一,因其具有良好的灵活性和可扩展性,被广泛应用于内容共享、分布式计算和协同交互等领域。但是由于P2P网络是由大量的自由用户节点构成,用户可以随意地加入和退出网络,使得服务质量不稳定,导致用户之间缺乏信任,而这种信任的缺失严重地阻碍了P2P网络的健康发展。
信任是一个用户对另一个用户的行为和能力的综合评价,用户间的信任是交往与合作的基础,是促进用户合作、鼓励共享资源的前提,直接影响P2P网络的环境与性能。通过引入信任机制,建立信任模型是解决P2P网络中信任问题的最佳方法之一。本文在充分借鉴心理学、社会学等学科中关于信任的知识,并深入分析信任的属性及其相互关系的基础上,在P2P文件共享应用背景下进行信任模型的研究,主要内容如下：
提出了基于影响的信任度计算。借鉴现实社会中人们观点形成的过程,对某个节点的观点(如：信任或不信任)是在自身观点的基础上,在他人观点的影响下形成的,即：信任的评估者对被评估者的信任评估是在自身直接经验的基础上,在他人推荐经验的影响下形成的。引入坚持度的概念,坚持度反应了观点形成过程的特征,通过坚持度实现对影响程度的量化。在本文提出的基于影响的信任度计算中,对他人信任的评估,是在自身交往的基础上(直接信任),在推荐者的影响下(推荐信任)建立起来的,通过坚持度,定义影响函数,最终实现基于影响的信任度计算。
基于兴趣的推荐者选择机制。借鉴现实社会中的人际关系现象,每个人都有自己的生活、交际圈,圈中人的兴趣、爱好基本相同,与这个圈子中的其他人交往、接触会更多,对某个人进行评价时,圈中人的意见更有价值。引入生物的遗传原理,通过文件DNA的遗传,记录文件的传播过程,获知文件的提供者和传播者,节点下载某个文件说明与此文件的提供者和传播者有类似相同的兴趣,通过不断的交往,自主节点逐步建立起与自己的兴趣组群,组中成员将作为推荐候选者,提高选择推荐者的效率。
恶意评价的遏止。提出了评价与恶意节点名单联动机制和评价反馈机制,引导节点基于自身利益尽量不进行恶意评价。将评价与恶意节点的设置联系起来,一方面,系统根据服务接受节点对服务提供节点的评价,将评价为恶意的节点放入恶意节点名单,避免再次与恶意节点交往,如果对提供正常服务的节点给予恶意评价,则将不能再接受正常节点的服务,使得恶意评价者逐渐被孤立,在一定程度上抑制恶意反馈行为,另一方面,运用评价反馈机制,接受服务的节点要向提供服务的节点反馈评价,对于恶意评价的节点,提供服务的节点将拒绝再次提供服务或降低服务质量,使得恶意评价者的利益受损,也可抑制恶意评价的发生。
文件真伪的初步判断。在下载文件前先下载文件DNA,根据文件遗传(传播)路径,获知文件的提供者和中间的传播者,根据中间传播者越多,文件质量相对越可靠的原则,并结合恶意集团成员名单,提供一个文件真伪初步判断的依据;并针对恶意集团可能的攻击给出了自适应算法。
基于兴趣组群的博弈互利激励。在兴趣组群的基础上,将与自主节点有过直接或间接交往的非恶意节点分为合作集和非合作集,在博弈论的指导下,本着互利与合作、惩罚与宽容的原则,对不同集中的节点,采取不同的策略,对于合作集中的节点,将提供较好的服务,对非合作集中的节点,则提供较差的服务或暂停服务,通过博弈,促使节点间相互合作;并且根据节点的行为表现,动态调整各集合内的节点。
P2P networks have been becoming one of the most important applications in the Internet nowadays. Due to the scalability and flexibility, they are widely used in content sharing, distributed computing, and collaborative work etc. However, P2P networks are constituted by large number free users. They can freely join and leave the networks making the unstable quality of service. It results in a lack of trust among users. The absence of trust blocks the progress of the P2P networks badly.
Trust is a comprehensive evaluation of the behavior and the ability of a user to another user. Trust among uses is the basis of the exchanges and cooperation, and the premise of cooperating and sharing. It directly impact on the P2P networks. By introducing trust mechanisms, trust model is one of the best way to solve the problem of trust in the P2P network. Learning from the knowledge of psychology, sociology and other disciplines on trust, we go into the trust models in P2P file-sharing application under a deep understanding of the nature of trust, and sorting out the relationship of the trust properties among them. The main achievements are as follow:
The process of trust calculating based on influence is proposed. Learning from the process formation of the people's view, the point of view to somebody (such as:trust or distrust) is formed based on own attitude with the influence of other people's attitude. In the same way, the evaluation of trust is formed on the basis of the direct trust of trustor with the influence of recommended trust of others. In this dissertation, trust calculating based on influence is realized by introducing the concept of insisting degrees to quantify the influence and defining influence function.
The selecting recommender mechanism based on interest. Learning from the interpersonal relationship in human society, attitudes of those who share the same interests are most valuable when evaluating a person. Through file's DNA by learning from the principle of genetics, acquiring the list of providers and disseminators, my interest groups are gradually established. The members of interest groups will serve as candidates. The efficiency of selecting recommender is improved by querying the members of interest groups.
Peers don't malicious evaluate as much as possible because of themselves benefit by proposing the mechanism of linkage between evaluation and malicious peers, and evaluation feedback mechanism. On the one hand, according to the poor evaluation of a peer receiving the service, the system will put the service provider into a malicious list and avoid contacting with malicious peers. A peer would gradually isolate with normal service providers if it malicious evaluates normal service providers. On the other hand, a peer accepting service should feed back evaluation to the service provider. Providers will refuse to provide service or reduce the quality of service to malicious valuators so that the interests of malicious valuators will be damaged. Occurrence of malicious evaluation be inhibited possibly.
Preliminary judgment of the authenticity of a file. Provider and disseminators are learned by the file's DNA. According to the principle that the quality of a file is relatively more reliable with more disseminators and the list of malicious group, a preliminary judgment of the file authenticity is provided. Adaptive algorithm is given for the possible attacks of malicious Group.
Reciprocal incentive based on interest groups. Interest groups are divided into a set of cooperation and non-cooperation by an autonomous peer itself. Under the guidance of game theory, different services are provided for corresponding sets with the spirit of reciprocal, punishment and tolerance. By gaming among peers, cooperation is promoted, and members of sets are dynamically adjusted according to its behavior.
信任是一个用户对另一个用户的行为和能力的综合评价,用户间的信任是交往与合作的基础,是促进用户合作、鼓励共享资源的前提,直接影响P2P网络的环境与性能。通过引入信任机制,建立信任模型是解决P2P网络中信任问题的最佳方法之一。本文在充分借鉴心理学、社会学等学科中关于信任的知识,并深入分析信任的属性及其相互关系的基础上,在P2P文件共享应用背景下进行信任模型的研究,主要内容如下：
提出了基于影响的信任度计算。借鉴现实社会中人们观点形成的过程,对某个节点的观点(如：信任或不信任)是在自身观点的基础上,在他人观点的影响下形成的,即：信任的评估者对被评估者的信任评估是在自身直接经验的基础上,在他人推荐经验的影响下形成的。引入坚持度的概念,坚持度反应了观点形成过程的特征,通过坚持度实现对影响程度的量化。在本文提出的基于影响的信任度计算中,对他人信任的评估,是在自身交往的基础上(直接信任),在推荐者的影响下(推荐信任)建立起来的,通过坚持度,定义影响函数,最终实现基于影响的信任度计算。
基于兴趣的推荐者选择机制。借鉴现实社会中的人际关系现象,每个人都有自己的生活、交际圈,圈中人的兴趣、爱好基本相同,与这个圈子中的其他人交往、接触会更多,对某个人进行评价时,圈中人的意见更有价值。引入生物的遗传原理,通过文件DNA的遗传,记录文件的传播过程,获知文件的提供者和传播者,节点下载某个文件说明与此文件的提供者和传播者有类似相同的兴趣,通过不断的交往,自主节点逐步建立起与自己的兴趣组群,组中成员将作为推荐候选者,提高选择推荐者的效率。
恶意评价的遏止。提出了评价与恶意节点名单联动机制和评价反馈机制,引导节点基于自身利益尽量不进行恶意评价。将评价与恶意节点的设置联系起来,一方面,系统根据服务接受节点对服务提供节点的评价,将评价为恶意的节点放入恶意节点名单,避免再次与恶意节点交往,如果对提供正常服务的节点给予恶意评价,则将不能再接受正常节点的服务,使得恶意评价者逐渐被孤立,在一定程度上抑制恶意反馈行为,另一方面,运用评价反馈机制,接受服务的节点要向提供服务的节点反馈评价,对于恶意评价的节点,提供服务的节点将拒绝再次提供服务或降低服务质量,使得恶意评价者的利益受损,也可抑制恶意评价的发生。
文件真伪的初步判断。在下载文件前先下载文件DNA,根据文件遗传(传播)路径,获知文件的提供者和中间的传播者,根据中间传播者越多,文件质量相对越可靠的原则,并结合恶意集团成员名单,提供一个文件真伪初步判断的依据;并针对恶意集团可能的攻击给出了自适应算法。
基于兴趣组群的博弈互利激励。在兴趣组群的基础上,将与自主节点有过直接或间接交往的非恶意节点分为合作集和非合作集,在博弈论的指导下,本着互利与合作、惩罚与宽容的原则,对不同集中的节点,采取不同的策略,对于合作集中的节点,将提供较好的服务,对非合作集中的节点,则提供较差的服务或暂停服务,通过博弈,促使节点间相互合作;并且根据节点的行为表现,动态调整各集合内的节点。
P2P networks have been becoming one of the most important applications in the Internet nowadays. Due to the scalability and flexibility, they are widely used in content sharing, distributed computing, and collaborative work etc. However, P2P networks are constituted by large number free users. They can freely join and leave the networks making the unstable quality of service. It results in a lack of trust among users. The absence of trust blocks the progress of the P2P networks badly.
Trust is a comprehensive evaluation of the behavior and the ability of a user to another user. Trust among uses is the basis of the exchanges and cooperation, and the premise of cooperating and sharing. It directly impact on the P2P networks. By introducing trust mechanisms, trust model is one of the best way to solve the problem of trust in the P2P network. Learning from the knowledge of psychology, sociology and other disciplines on trust, we go into the trust models in P2P file-sharing application under a deep understanding of the nature of trust, and sorting out the relationship of the trust properties among them. The main achievements are as follow:
The process of trust calculating based on influence is proposed. Learning from the process formation of the people's view, the point of view to somebody (such as:trust or distrust) is formed based on own attitude with the influence of other people's attitude. In the same way, the evaluation of trust is formed on the basis of the direct trust of trustor with the influence of recommended trust of others. In this dissertation, trust calculating based on influence is realized by introducing the concept of insisting degrees to quantify the influence and defining influence function.
The selecting recommender mechanism based on interest. Learning from the interpersonal relationship in human society, attitudes of those who share the same interests are most valuable when evaluating a person. Through file's DNA by learning from the principle of genetics, acquiring the list of providers and disseminators, my interest groups are gradually established. The members of interest groups will serve as candidates. The efficiency of selecting recommender is improved by querying the members of interest groups.
Peers don't malicious evaluate as much as possible because of themselves benefit by proposing the mechanism of linkage between evaluation and malicious peers, and evaluation feedback mechanism. On the one hand, according to the poor evaluation of a peer receiving the service, the system will put the service provider into a malicious list and avoid contacting with malicious peers. A peer would gradually isolate with normal service providers if it malicious evaluates normal service providers. On the other hand, a peer accepting service should feed back evaluation to the service provider. Providers will refuse to provide service or reduce the quality of service to malicious valuators so that the interests of malicious valuators will be damaged. Occurrence of malicious evaluation be inhibited possibly.
Preliminary judgment of the authenticity of a file. Provider and disseminators are learned by the file's DNA. According to the principle that the quality of a file is relatively more reliable with more disseminators and the list of malicious group, a preliminary judgment of the file authenticity is provided. Adaptive algorithm is given for the possible attacks of malicious Group.
Reciprocal incentive based on interest groups. Interest groups are divided into a set of cooperation and non-cooperation by an autonomous peer itself. Under the guidance of game theory, different services are provided for corresponding sets with the spirit of reciprocal, punishment and tolerance. By gaming among peers, cooperation is promoted, and members of sets are dynamically adjusted according to its behavior.
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